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One main challenge in federated learning is the large communication cost of exchanging weight updates from clients to the server at each round.
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Jorma Rissanen and Glen G Langdon · 1979
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Raphail Krichevsky and Victor Trofimov · 1981
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Learning multiple layers of features from tiny images
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Li Deng · 2012
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Sparse communication for distributed gradient descent
Alham Aji and Kenneth Heafield · 2017
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Ternary neural networks for resource-efficient AI applications
Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot · 2017
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QSGD: Communication-efficient SGD via gradient quantization and encoding
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
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EMNIST: Extending MNIST to handwritten letters
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and Andre Van Schaik · 2017
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Rényi differential privacy
Ilya Mironov · 2017
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Distributed mean estimation with limited communication
Ananda Theertha Suresh, X Yu Felix, Sanjiv Kumar, and H Brendan McMahan · 2017
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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
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Privacy amplification by subsampling: tight analyses via couplings and divergences
B. Balle, G Barthe, and M. Gaboardi · 2018
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signsgd: Compressed optimisation for non-convex problems
Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, and Animashree Anandkumar · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Deep gradient compression: Reducing the communication bandwidth for distributed training
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and Bill Dally · 2018
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Atomo: Communication-efficient learning via atomic sparsification
Hongyi Wang, Scott Sievert, Shengchao Liu, Zachary Charles, Dimitris Papailiopoulos, and Stephen Wright · 2018
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Amplification by shuffling: From local to central differential privacy via anonymity
Úlfar Erlingsson, Vitaly Feldman, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and Abhradeep Thakurta · 2019
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Robust and communication-efficient federated learning from non-iid data
Felix Sattler, Simon Wiedemann, Klaus-Robert Müller, and Wojciech Samek · 2019
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Powersgd: Practical low-rank gradient compression for distributed optimization
Thijs Vogels, Sai Praneeth Karimireddy, and Martin Jaggi · 2019
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Subsampled renyi differential privacy and analytical moments accountant
Yu-Xiang Wang, Borja Balle, and Shiva Prasad Kasiviswanathan · 2019
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Deconstructing lottery tickets: Zeros, signs, and the supermask
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
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Privacy amplification via random check-ins
Federated learning with compression: Unified analysis and sharp guarantees
Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari, and Mehrdad Mahdavi · 2021
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Privacy amplification via bernoulli sampling
Jacob Imola and Kamalika Chaudhuri · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Fedmask: Joint computation and communication-efficient personalized federated learning via heterogeneous masking
Ang Li, Jingwei Sun, Xiao Zeng, Mi Zhang, Hai Li, and Yiran Chen · 2021
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Fedprune: Personalized and communication-efficient federated learning on non-iid data
Yang Liu, Yi Zhao, Guangmeng Zhou, and Ke Xu · 2021
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Borja Balle, Peter Kairouz, Brendan McMahan, Om Thakkar, and Abhradeep Guha Thakurta · 2020
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rtop-k: A statistical estimation approach to distributed SGD
Leighton Pate Barnes, Huseyin A Inan, Berivan Isik, and Ayfer Özgür · 2020
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Multi-prize lottery ticket hypothesis: Finding accurate binary neural networks by pruning a randomly weighted network
James Diffenderfer and Bhavya Kailkhura · 2020
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Fedsketch: Communication-efficient and private federated learning via sketching
Farzin Haddadpour, Belhal Karimi, Ping Li, and Xiaoyun Li · 2020
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Characterising bias in compressed models
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, and Emily Denton · 2020
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Dynamic sampling and selective masking for communication-efficient federated learning
Shaoxiong Ji, Wenqi Jiang, Anwar Walid, and Xue Li · 2020
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Ang Li, Jingwei Sun, Binghui Wang, Lin Duan, Sicheng Li, Yiran Chen, and Hai Li · 2020
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Hamid Mozaffari, Virat Shejwalkar, and Amir Houmansadr · 2021
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Fedprune: Towards inclusive federated learning
Muhammad Tahir Munir, Muhammad Mustansar Saeed, Mahad Ali, Zafar Ayyub Qazi, and Ihsan Ayyub Qazi · 2021
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Time-correlated sparsification for communication-efficient federated learning
Emre Ozfatura, Kerem Ozfatura, and Deniz Gündüz · 2021
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Communication-efficient and personalized federated lottery ticket learning
Sejin Seo, Seung-Woo Ko, Jihong Park, Seong-Lyun Kim, and Mehdi Bennis · 2021
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Drive: one-bit distributed mean estimation
Shay Vargaftik, Ran Ben-Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, and Michael Mitzenmacher · 2021
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Adaptive dynamic pruning for non-iid federated learning
Sixing Yu, Phuong Nguyen, Ali Anwar, and Ali Jannesari · 2021
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Federated sparse training: Lottery aware model compression for resource constrained edge
Sara Babakniya, Souvik Kundu, Saurav Prakash, Yue Niu, and Salman Avestimehr · 2022
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Rong Dai, Li Shen, Fengxiang He, Xinmei Tian, and Dacheng Tao · 2022
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Privacy amplification via random participation in federated learning
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An information-theoretic justification for model pruning
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Model pruning enables efficient federated learning on edge devices
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Optimizing the communication-accuracy trade-off in federated learning with rate-distortion theory
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Masked training of neural networks with partial gradients
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Hidenseek: Federated lottery ticket via server-side pruning and sign supermask
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