Fetching the paper…
Reading the bibliography…
Federated learning and analytics are a distributed approach for collaboratively learning models (or statistics) from decentralized data, motivated by and designed for privacy protection.
Towards federated learning at scale: System design
Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloé M Kiddon, Jakub Konečný, Stefano Mazzocchi, Brendan McMahan, Timon Van Overveldt, David Petrou, Daniel Ramage, and Jason Roselander · 1902
Earlier work this paper cites.
Stochastic distributed learning with gradient quantization and variance reduction
Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Sebastian Stich, and Peter Richtárik · 1904
Earlier work this paper cites.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan and Vitaly Shmatikov · 1905
Earlier work this paper cites.
Natural compression for distributed deep learning
Samuel Horváth, Chen-Yu Ho, Ľudovít Horváth, Atal Narayan Sahu, Marco Canini, and Peter Richtárik · 1905
Earlier work this paper cites.
On the convergence of FedAvg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 1907
Earlier work this paper cites.
Federated evaluation of on-device personalization
Kangkang Wang, Rajiv Mathews, Chloé Kiddon, Hubert Eichner, Françoise Beaufays, and Daniel Ramage · 1910
Earlier work this paper cites.
Eavesdrop the composition proportion of training labels in federated learning
Lixu Wang, Shichao Xu, Xiao Wang, and Qi Zhu · 1910
Earlier work this paper cites.
Compression of individual sequences via variable-rate coding
Jacob Ziv and Abraham Lempel · 1978
Earlier work this paper cites.
Arithmetic coding
Jorma Rissanen and Glen G Langdon · 1979
Earlier work this paper cites.
Distributed asynchronous deterministic and stochastic gradient optimization algorithms
J. Tsitsiklis, D. Bertsekas, and M. Athans · 1986
Earlier work this paper cites.
Bayesian methods for adaptive models
David JC MacKay · 1992
Earlier work this paper cites.
On the momentum term in gradient descent learning algorithms
Ning Qian · 1999
Earlier work this paper cites.
Is local sgd better than minibatch sgd?
Blake Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, and Nathan Srebro · 2002
Earlier work this paper cites.
Adaptive personalized federated learning
Yuyang Deng, Mohammad Mahdi Kamani, and Mehrdad Mahdavi · 2003
Earlier work this paper cites.
Regularized multi–task learning
Theodoros Evgeniou and Massimiliano Pontil · 2004
Earlier work this paper cites.
Fednas: Federated deep learning via neural architecture search
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2004
Earlier work this paper cites.
Hierarchically fair federated learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly, and Mohan Kankanhalli · 2004
Earlier work this paper cites.
Byzantine-resilient SGD in high dimensions on heterogeneous data
Deepesh Data and Suhas N. Diggavi · 2005
Earlier work this paper cites.
Squarm-sgd: Communication-efficient momentum SGD for decentralized optimization
Navjot Singh, Deepesh Data, Jemin George, and Suhas N. Diggavi · 2005
Earlier work this paper cites.
Fedpd: A federated learning framework with optimal rates and adaptivity to non-iid data
Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, and Yang Liu · 2005
Earlier work this paper cites.
Stability of stochastic gradient descent on nonsmooth convex losses
Raef Bassily, Vitaly Feldman, Cristóbal Guzmán, and Kunal Talwar · 2006
Earlier work this paper cites.
Pachinko allocation: DAG-structured mixture models of topic correlations
Wei Li and Andrew McCallum · 2006
Earlier work this paper cites.
Minibatch vs local SGD for heterogeneous distributed learning
Blake Woodworth, Kumar Kshitij Patel, and Nathan Srebro · 2006
Earlier work this paper cites.
Breaking the communication-privacy-accuracy trilemma
Wei-Ning Chen, Peter Kairouz, and Ayfer Özgür · 2007
Earlier work this paper cites.
Fedml: A research library and benchmark for federated machine learning
Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, and Salman Avestimehr · 2007
Earlier work this paper cites.
Learning from mixtures of private and public populations
Raef Bassily, Shay Moran, and Anupama Nandi · 2008
Earlier work this paper cites.
Mime: Mimicking centralized stochastic algorithms in federated learning
Sai Praneeth Karimireddy, Martin Jaggi, Satyen Kale, Mehryar Mohri, Sashank J Reddi, Sebastian U Stich, and Ananda Theertha Suresh · 2008
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
Bandwidth optimal all-reduce algorithms for clusters of workstations
Pitch Patarasuk and Xin Yuan · 2009
Earlier work this paper cites.
Optimal client sampling for federated learning
Wenlin Chen, Samuel Horváth, and Peter Richtárik · 2010
Earlier work this paper cites.
Distributed training strategies for the structured perceptron
Ryan McDonald, Keith Hall, and Gideon Mann · 2010
Earlier work this paper cites.
Error compensated distributed SGD can be accelerated
Xun Qian, Peter Richtárik, and Tong Zhang · 2010
Earlier work this paper cites.
Differentially private empirical risk minimization
Kamalika Chaudhuri, Claire Monteleoni, and Anand D Sarwate · 2011
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
Earlier work this paper cites.
Local SGD: Unified theory and new efficient methods
Eduard Gorbunov, Filip Hanzely, and Peter Richtárik · 2011
Earlier work this paper cites.
What can we learn privately?
Shiva Prasad Kasiviswanathan, Homin K Lee, Kobbi Nissim, Sofya Raskhodnikova, and Adam Smith · 2011
Earlier work this paper cites.
Optimal distributed online prediction using mini-batches
Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao · 2012
Earlier work this paper cites.
Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
Earlier work this paper cites.
Local privacy and statistical minimax rates
John C Duchi, Michael I Jordan, and Martin J Wainwright · 2013
Earlier work this paper cites.
Gradient methods for minimizing composite functions
Yu. Nesterov · 2013
Earlier work this paper cites.
Communication efficient distributed optimization using an approximate Newton-type method
Ohad Shamir, Nathan Srebro, and Tong Zhang · 2013
Earlier work this paper cites.
On the importance of initialization and momentum in deep learning
Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton · 2013
Earlier work this paper cites.
Private empirical risk minimization: Efficient algorithms and tight error bounds
Raef Bassily, Adam Smith, and Abhradeep Thakurta · 2014
Earlier work this paper cites.
Fast distributed coordinate descent for minimizing non-strongly convex losses
Olivier Fercoq, Zheng Qu, Peter Richtárik, and Martin Takáč · 2014
Earlier work this paper cites.
(near) dimension independent risk bounds for differentially private learning
Prateek Jain and Abhradeep Guha Thakurta · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Parallel training of dnns with natural gradient and parameter averaging
Daniel Povey, Xiaohui Zhang, and Sanjeev Khudanpur · 2014
Earlier work this paper cites.
1-bit stochastic gradient descent and its application to data-parallel distributed training of speech DNNs
Frank Seide, Hao Fu, Jasha Droppo, Gang Li, and Dong Yu · 2014
Earlier work this paper cites.
Communication complexity of distributed convex learning and optimization
Yossi Arjevani and Ohad Shamir · 2015
Earlier work this paper cites.
SparkNet: Training deep networks in spark
Philipp Moritz, Robert Nishihara, Ion Stoica, and Michael I Jordan · 2015
Earlier work this paper cites.
Scalable distributed dnn training using commodity gpu cloud computing
Nikko Strom · 2015
Earlier work this paper cites.
Experiments on parallel training of deep neural network using model averaging
Hang Su and Haoyu Chen · 2015
Earlier work this paper cites.
Disco: Distributed optimization for self-concordant empirical loss
Yuchen Zhang and Xiao Lin · 2015
Earlier work this paper cites.
Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
Earlier work this paper cites.
Scalable training of deep learning machines by incremental block training with intra-block parallel optimization and blockwise model-update filtering
Kai Chen and Qiang Huo · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Discrete distribution estimation under local privacy
Peter Kairouz, Keith Bonawitz, and Daniel Ramage · 2016
Earlier work this paper cites.
Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2016
Earlier work this paper cites.
AIDE: Fast and communication efficient distributed optimization
Sashank J Reddi, Jakub Konečný, Peter Richtárik, Barnabás Póczós, and Alex Smola · 2016
Earlier work this paper cites.
Distributed coordinate descent method for learning with big data
Peter Richtárik and Martin Takáč · 2016
Earlier work this paper cites.
Bayes and big data: The consensus monte carlo algorithm
Steven L Scott, Alexander W Blocker, Fernando V Bonassi, Hugh A Chipman, Edward I George, and Robert E McCulloch · 2016
Earlier work this paper cites.
On the convergence of decentralized gradient descent
Kun Yuan, Qing Ling, and Wotao Yin · 2016
Earlier work this paper cites.
Parallel SGD: When does averaging help?
Jian Zhang, Christopher De Sa, Ioannis Mitliagkas, and Christopher Ré · 2016
Earlier work this paper cites.
QSGD: Communication-efficient SGD via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Earlier work this paper cites.
Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
Earlier work this paper cites.
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer · 2017
Earlier work this paper cites.
Practical secure aggregation for privacy-preserving machine learning
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth · 2017
Earlier work this paper cites.
Parle: parallelizing stochastic gradient descent
Pratik Chaudhari, Carlo Baldassi, Riccardo Zecchina, Stefano Soatto, Ameet Talwalkar, and Adam Oberman · 2017
Earlier work this paper cites.
Distributed statistical machine learning in adversarial settings: Byzantine gradient descent
Yudong Chen, Lili Su, and Jiaming Xu · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Earlier work this paper cites.
Stochastic composite least-squares regression with convergence rate O(1/n)
Nicolas Flammarion and Francis Bach · 2017
Earlier work this paper cites.
Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
Earlier work this paper cites.
Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne · 2017
Earlier work this paper cites.
Distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server
Leonard Hasenclever, Stefan Webb, Thibaut Lienart, Sebastian Vollmer, Balaji Lakshminarayanan, Charles Blundell, and Yee Whye Teh · 2017
Earlier work this paper cites.
How to escape saddle points efficiently
Chi Jin, Rong Ge, Praneeth Netrapalli, Sham M Kakade, and Michael I Jordan · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Earlier work this paper cites.
Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu · 2017
Earlier work this paper cites.
Distributed learning for cooperative inference, 2017
Angelia Nedić, Alex Olshevsky, and César A. Uribe · 2017
Earlier work this paper cites.
Large-scale image retrieval with attentive deep local features
Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han · 2017
Earlier work this paper cites.
A hybrid deep learning architecture for privacy-preserving mobile analytics, 2017
Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, and Hamed Haddadi · 2017
Earlier work this paper cites.
Is interaction necessary for distributed private learning?
Adam Smith, Abhradeep Thakurta, and Jalaj Upadhyay · 2017
Earlier work this paper cites.
Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei W Koh, and Percy S Liang · 2017
Earlier work this paper cites.
Distributed mean estimation with limited communication
Ananda Theertha Suresh, Felix X Yu, Sanjiv Kumar, and H Brendan McMahan · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Terngrad: Ternary gradients to reduce communication in distributed deep learning
Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, and Hai Li · 2017
Cited alongside, same era.
cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
Cited alongside, same era.
Privacy amplification via random check-ins
Borja Balle, Peter Kairouz, Brendan McMahan, Om Dipakbhai Thakkar, and Abhradeep Thakurta · 2020
Later among the works it cites.
Secure single-server aggregation with (poly)logarithmic overhead
James Henry Bell, Kallista A. Bonawitz, Adrià Gascón, Tancrède Lepoint, and Mariana Raykova · 2020
Later among the works it cites.
Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane · 2020
Later among the works it cites.
On biased compression for distributed learning
Aleksandr Beznosikov, Samuel Horváth, Peter Richtárik, and Mher Safaryan · 2020
Later among the works it cites.
Federated learning with hierarchical clustering of local updates to improve training on non-iid data
Christopher Briggs, Zhong Fan, and Peter Andras · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
signsgd: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
Cited alongside, same era.
Leaf: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
Cited alongside, same era.
Slow and stale gradients can win the race: Error-runtime trade-offs in distributed sgd
Sanghamitra Dutta, Gauri Joshi, Soumyadip Ghosh, Parijat Dube, and Priya Nagpurkar · 2018
Cited alongside, same era.
NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision
Biyi Fang, Xiao Zeng, and Mi Zhang · 2018
Cited alongside, same era.
Distributed learning of deep neural network over multiple agents
Otkrist Gupta and Ramesh Raskar · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
Cited alongside, same era.
Later among the works it cites.
On the outsized importance of learning rates in local update methods
Zachary Charles and Jakub Konečný · 2020
Later among the works it cites.
When does gradient descent with logistic loss find interpolating two-layer networks?
Niladri S Chatterji, Philip M Long, and Peter L Bartlett · 2020
Later among the works it cites.
Yae Jee Cho, Jianyu Wang, and Gauri Joshi · 2020
Later among the works it cites.
Interaction is necessary for distributed learning with privacy or communication constraints
Yuval Dagan and Vitaly Feldman · 2020
Later among the works it cites.
Personalized federated learning with moreau envelopes
Canh T Dinh, Nguyen H Tran, and Tuan Dung Nguyen · 2020
Later among the works it cites.
Encode, shuffle, analyze privacy revisited: formalizations and empirical evaluation
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Shuang Song, Kunal Talwar, and Abhradeep Thakurta · 2020
Later among the works it cites.
Personalized federated learning: A meta-learning approach
Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar · 2020
Later among the works it cites.
Local model poisoning attacks to byzantine-robust federated learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia, and Neil Gong · 2020
Later among the works it cites.
Private stochastic convex optimization: optimal rates in linear time
Vitaly Feldman, Tomer Koren, and Kunal Talwar · 2020
Later among the works it cites.
Federated analytics: Collaborative data science without data collection
Google · 2020
Later among the works it cites.
Federated learning of a mixture of global and local models
Filip Hanzely and Peter Richtárik · 2020
Later among the works it cites.
Lower bounds and optimal algorithms for personalized federated learning
Filip Hanzely, Slavomír Hanzely, Samuel Horváth, and Peter Richtárik · 2020
Later among the works it cites.
Training keyword spotting models on non-iid data with federated learning
Andrew Hard, Kurt Partridge, Cameron Nguyen, Niranjan Subrahmanya, Aishanee Shah, Pai Zhu, Ignacio Lopez Moreno, and Rajiv Mathews · 2020
Later among the works it cites.
The non-iid data quagmire of decentralized machine learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, and Phillip Gibbons · 2020
Later among the works it cites.
Federated visual classification with real-world data distribution
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown · 2020
Later among the works it cites.
FedMGDA+: Federated learning meets multi-objective optimization
Zeou Hu, Kiarash Shaloudegi, Guojun Zhang, and Yaoliang Yu · 2020
Later among the works it cites.
Intel and consilient join forces to fight financial fraud with ai
Intel and Consilient · 2020
Later among the works it cites.
Dimension independence in unconstrained private erm via adaptive preconditioning
Peter Kairouz, Mónica Ribero, Keith Rush, and Abhradeep Thakurta · 2020
Later among the works it cites.
Tighter theory for local SGD on identical and heterogeneous data
Ahmed Khaled, Konstantin Mishchenko, and Peter Richtárik · 2020
Later among the works it cites.
A unified theory of decentralized SGD with changing topology and local updates
Anastasia Koloskova, Nicolas Loizou, Sadra Boreiri, Martin Jaggi, and Sebastian U. Stich · 2020
Later among the works it cites.
Optimal and practical algorithms for smooth and strongly convex decentralized optimization
Dmitry Kovalev, Adil Salim, and Peter Richtárik · 2020
Later among the works it cites.
Device heterogeneity in federated learning: A superquantile approach
Yassine Laguel, Krishna Pillutla, Jérôme Malick, and Zaid Harchaoui · 2020
Later among the works it cites.
Think locally, act globally: Federated learning with local and global representations
Paul Pu Liang, Terrance Liu, Liu Ziyin, Nicholas B. Allen, Randy P. Auerbach, David Brent, Ruslan Salakhutdinov, and Louis-Philippe Morency · 2020
Later among the works it cites.
Federated learning in mobile edge networks: A comprehensive survey
Wei Yang Bryan Lim, Nguyen Cong Luong, Dinh Thai Hoang, Yutao Jiao, Ying-Chang Liang, Qiang Yang, Dusit Niyato, and Chunyan Miao · 2020
Later among the works it cites.
Ensemble distillation for robust model fusion in federated learning
Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi · 2020
Later among the works it cites.
From local sgd to local fixed-point methods for federated learning
Grigory Malinovskiy, Dmitry Kovalev, Elnur Gasanov, Laurent Condat, and Peter Richtárik · 2020
Later among the works it cites.
Three approaches for personalization with applications to federated learning
Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh · 2020
Later among the works it cites.
Melloddy project meets its year one objective: Deployment of the world’s first secure platform for multi-task federated learning in drug discovery among 10 pharmaceutical companies
MELLODDY · 2020
Later among the works it cites.
Triaging covid-19 patients: 20 hospitals in 20 days build ai model that predicts oxygen needs
NVIDIA · 2020
Later among the works it cites.
Story of the 1st federated learning model at owkin
Owkin · 2020
Later among the works it cites.
Decentralized langevin dynamics for bayesian learning
Anjaly Parayil, He Bai, Jemin George, and Prudhvi Gurram · 2020
Later among the works it cites.
Coded computing for low-latency federated learning over wireless edge networks
Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz, A. Salman Avestimehr, and Nageen Himayat · 2020
Later among the works it cites.
Error compensated loopless SVRG for distributed optimization
Xun Qian, Hanze Dong, Peter Richtárik, and Tong Zhang · 2020
Later among the works it cites.
FedJAX: Federated learning simulation with JAX, 2020
Jae Hun Ro, Ananda Theertha Suresh, and Ke Wu · 2020
Later among the works it cites.
Fetchsgd: Communication-efficient federated learning with sketching
Daniel Rothchild, Ashwinee Panda, Enayat Ullah, Nikita Ivkin, Ion Stoica, Vladimir Braverman, Joseph Gonzalez, and Raman Arora · 2020
Later among the works it cites.
Towards flexible device participation in federated learning for non-iid data
Yichen Ruan, Xiaoxi Zhang, Shu-Che Liang, and Carlee Joe-Wong · 2020
Later among the works it cites.
How good is sgd with random shuffling?
Itay Safran and Ohad Shamir · 2020
Later among the works it cites.
Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints
Felix Sattler, Klaus-Robert Müller, and Wojciech Samek · 2020
Later among the works it cites.
Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Gang Huang, Pan Zhou, Fei Wu, and Chao Wu · 2020
Later among the works it cites.
Model fusion via optimal transport
Sidak Pal Singh and Martin Jaggi · 2020
Later among the works it cites.
Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning
Jinhyun So, Basak Guler, and A Salman Avestimehr · 2020
Later among the works it cites.
Data poisoning attacks on federated machine learning
Gan Sun, Yang Cong, Jiahua Dong, Qiang Wang, and Ji Liu · 2020
Later among the works it cites.
Tensorflow federated, 2020
TFF · 2020
Later among the works it cites.
Expectation propagation as a way of life: A framework for bayesian inference on partitioned data
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P Cunningham, David Schiminovich, and Christian P Robert · 2020
Later among the works it cites.
Enhancing privacy via hierarchical federated learning
Aidmar Wainakh, Alejandro Sanchez Guinea, Tim Grube, and Max Mühlhäuser · 2020
Later among the works it cites.
Utilization of fate in anti money laundering through multiple banks
WeBank · 2020
Later among the works it cites.
A framework for evaluating gradient leakage attacks in federated learning
Wenqi Wei, Ling Liu, Margaret Loper, Ka-Ho Chow, Mehmet Emre Gursoy, Stacey Truex, and Yanzhao Wu · 2020
Later among the works it cites.
Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval
Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim · 2020
Later among the works it cites.
Compressed communication for distributed deep learning: Survey and quantitative evaluation
Hang Xu, Chen-Yu Ho, Ahmed M Abdelmoniem, Aritra Dutta, El Houcine Bergou, Konstantinos Karatsenidis, Marco Canini, and Panos Kalnis · 2020
Later among the works it cites.
Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
Later among the works it cites.
Federated accelerated stochastic gradient descent
Honglin Yuan and Tengyu Ma · 2020
Later among the works it cites.
Federated composite optimization
Honglin Yuan, Manzil Zaheer, and Sashank Reddi · 2020
Later among the works it cites.
Deep leakage from gradients
Ligeng Zhu and Song Han · 2020
Later among the works it cites.
Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama · 2021
Closest in time.
On the utility of gradient compression in distributed training systems
Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, and Dimitris Papailiopoulos · 2021
Closest in time.
Byzantine-resilient non-convex stochastic gradient descent
Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li, and Dan Alistarh · 2021
Closest in time.
Convergence and accuracy trade-offs in federated learning and meta-learning
Zachary Charles and Jakub Konečnỳ · 2021
Closest in time.
On large-cohort training for federated learning
Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, and Virginia Smith · 2021
Closest in time.
Data encoding for byzantine-resilient distributed optimization
Deepesh Data, Linqi Song, and Suhas N. Diggavi · 2021
Closest in time.
Heterogeneity for the win: One-shot federated clustering
Don Kurian Dennis, Tian Li, and Virginia Smith · 2021
Closest in time.
Shuffled model of differential privacy in federated learning
Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, and Ananda Theertha Suresh · 2021
Closest in time.
Your voice and audio data stays private while google assistant improves
Google · 2021
Closest in time.
Decentralized stochastic gradient langevin dynamics and hamiltonian monte carlo, 2021
Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu, and Lingjiong Zhu · 2021
Closest in time.
Personalized federated learning: A unified framework and universal optimization techniques
Filip Hanzely, Boxin Zhao, and Mladen Kolar · 2021
Closest in time.
A better alternative to error feedback for communication-efficient distributed learning
Samuel Horváth and Peter Richtárik · 2021
Closest in time.
Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout
Samuel Horváth, Stefanos Laskaridis, Mario Almeida, Ilias Leontiadis, Stylianos I Venieris, and Nicholas D Lane · 2021
Closest in time.
Personalized cross-silo federated learning on non-iid data
Yutao Huang, Lingyang Chu, Zirui Zhou, Lanjun Wang, Jiangchuan Liu, Jian Pei, and Yong Zhang · 2021
Closest in time.
Intel® open federated learning, 2021
Intel® · 2021
Closest in time.
Learning from history for byzantine robust optimization
Sai Praneeth Karimireddy, Lie He, and Martin Jaggi · 2021
Closest in time.
Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing
Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina Balcan, Virginia Smith, and Ameet Talwalkar · 2021
Closest in time.
A linearly convergent algorithm for decentralized optimization: Sending less bits for free!
Dmitry Kovalev, Anastasia Koloskova, Martin Jaggi, Peter Richtárik, and Sebastian U. Stich · 2021
Closest in time.
Fedscale: Benchmarking model and system performance of federated learning
Fan Lai, Yinwei Dai, Xiangfeng Zhu, and Mosharaf Chowdhury · 2021
Closest in time.
Qupel: Quantized personalization with applications to federated learning
Kaan Ozkara, Navjot Singh, Deepesh Data, and Suhas N. Diggavi · 2021
Closest in time.
Federated evaluation and tuning for on-device personalization: System design & applications
Matthias Paulik, Matt Seigel, Henry Mason, Dominic Telaar, Joris Kluivers, Rogier van Dalen, Chi Wai Lau, Luke Carlson, Filip Granqvist, Chris Vandevelde, et al · 2021
Closest in time.
Adaptive federated optimization
Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečný, Sanjiv Kumar, and Hugh Brendan McMahan · 2021
Closest in time.
Federated reconstruction: Partially local federated learning
Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash · 2021
Closest in time.