Fetching the paper…
Reading the bibliography…
Foundation Models (FMs) have revolutionized machine learning with their adaptability and high performance across tasks; yet, their integration into Federated Learning (FL) is challenging due to substantial communication overhead from their extensive parameterization.
Arithmetic coding
J. Rissanen and G. G. Langdon · 1979
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
Earlier work this paper cites.
Food-101 – mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
Jakub Konečný, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon · 2016
Earlier work this paper cites.
Sparse communication for distributed gradient descent
Alham Fikri Aji and Kenneth Heafield · 2017
Earlier work this paper cites.
Qsgd: Communication-efficient sgd via gradient quantization and encoding, 2017
Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, and Milan Vojnovic · 2017
Earlier work this paper cites.
The sample size required in importance sampling, 2017
Sourav Chatterjee and Persi Diaconis · 2017
Earlier work this paper cites.
Emnist: an extension of mnist to handwritten letters, 2017
Gregory Cohen, Saeed Afshar, Jonathan Tapson, and André van Schaik · 2017
Earlier work this paper cites.
Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2017
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2017
Earlier work this paper cites.
Communication-Efficient Learning of Deep Networks from Decentralized Data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
Earlier work this paper cites.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Earlier work this paper cites.
Minimal random code learning: Getting bits back from compressed model parameters, 2018
Marton Havasi, Robert Peharz, and José Miguel Hernández-Lobato · 2018
Earlier work this paper cites.
Piggyback: Adapting a single network to multiple tasks by learning to mask weights, 2018
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
Cited alongside, same era.
The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2019
Jonathan Frankle and Michael Carbin · 2019
Cited alongside, same era.
Flower: A friendly federated learning research framework
Daniel J Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Titouan Parcollet, and Nicholas D Lane · 2020
Cited alongside, same era.
Xor filters: Faster and smaller than bloom and cuckoo filters
Thomas Mueller Graf and Daniel Lemire · 2020
Cited alongside, same era.
Lotteryfl: Personalized and communication-efficient federated learning with lottery ticket hypothesis on non-iid datasets, 2020
Ang Li, Jingwei Sun, Binghui Wang, Lin Duan, Sicheng Li, Yiran Chen, and Hai Li · 2020
Cited alongside, same era.
Drive: One-bit distributed mean estimation, 2021
Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben-Itzhak, and Michael Mitzenmacher · 2021
Later among the works it cites.
Optimal client sampling for federated learning, 2022
Wenlin Chen, Samuel Horvath, and Peter Richtarik · 2022
Later among the works it cites.
Binary fuse filters: Fast and smaller than xor filters
Thomas Mueller Graf and Daniel Lemire · 2022
Later among the works it cites.
Masked training of neural networks with partial gradients, 2022
Amirkeivan Mohtashami, Martin Jaggi, and Sebastian U. Stich · 2022
Later among the works it cites.
Frl: Federated rank learning, 2022
Hamid Mozaffari, Virat Shejwalkar, and Amir Houmansadr · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep gradient compression: Reducing the communication bandwidth for distributed training, 2020
Yujun Lin, Song Han, Huizi Mao, Yu Wang, and William J. Dally · 2020
Cited alongside, same era.
What’s hidden in a randomly weighted neural network?, 2020
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
Cited alongside, same era.
Attack of the tails: Yes, you really can backdoor federated learning
Hongyi Wang, Kartik Sreenivasan, Shashank Rajput, Harit Vishwakarma, Saurabh Agarwal, Jy-yong Sohn, Kangwook Lee, and Dimitris Papailiopoulos · 2020
Cited alongside, same era.
Masking as an efficient alternative to finetuning for pretrained language models
Mengjie Zhao, Tao Lin, Fei Mi, Martin Jaggi, and Hinrich Schütze · 2020
Cited alongside, same era.
Deconstructing lottery tickets: Zeros, signs, and the supermask, 2020
Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2020
Cited alongside, same era.
Slot machines: Discovering winning combinations of random weights in neural networks, 2021
Maxwell Mbabilla Aladago and Lorenzo Torresani · 2021
Cited alongside, same era.
Deepreduce: A sparse-tensor communication framework for distributed deep learning, 2021
Kelly Kostopoulou, Hang Xu, Aritra Dutta, Xin Li, Alexandros Ntoulas, and Panos Kalnis · 2021
Cited alongside, same era.
Aliaksandra Shysheya, John Bronskill, Massimiliano Patacchiola, Sebastian Nowozin, and Richard E Turner · 2022
Later among the works it cites.
Patches are all you need?, 2022
Asher Trockman and J. Zico Kolter · 2022
Later among the works it cites.
Hidenseek: Federated lottery ticket via server-side pruning and sign supermask, 2022
Anish K. Vallapuram, Pengyuan Zhou, Young D. Kwon, Lik Hang Lee, Hengwei Xu, and Pan Hui · 2022
Later among the works it cites.
EDEN: Communication-efficient and robust distributed mean estimation for federated learning
Shay Vargaftik, Ran Ben Basat, Amit Portnoy, Gal Mendelson, Yaniv Ben Itzhak, and Michael Mitzenmacher · 2022
Later among the works it cites.
Murmurhash3
Austin Appleby · 2023
Closest in time.
Scaling vision transformers to 22 billion parameters, 2023
Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, and Neil Houlsby · 2023
Closest in time.
Fedcode: Communication-efficient federated learning via transferring codebooks, 2023
Saeed Khalilian, Vasileios Tsouvalas, Tanir Ozcelebi, and Nirvana Meratnia · 2023
Closest in time.
Dinov2: Learning robust visual features without supervision, 2023
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Mahmoud Assran, Nicolas Ballas, Wojciech Galuba, Russell Howes, Po-Yao Huang, Shang-Wen Li, Ishan Misra, Michael Rabbat, Vasu Sharma, Gabriel Synnaeve, Hu Xu, Hervé Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
Closest in time.