Optimal Gradient Compression for Distributed and Federated Learning

Optimal Gradient Compression for Distributed and Federated Learning

7 October 2020

Alyazeed Albasyoni

Peter Richtárik

Papers citing "Optimal Gradient Compression for Distributed and Federated Learning"

18 / 18 papers shown

Title
Accelerated Distributed Optimization with Compression and Error Feedback Yuan Gao Anton Rodomanov Jeremy Rack Sebastian U. Stich 51 0 0 11 Mar 2025
Communication-Efficient Device Scheduling for Federated Learning Using Lyapunov Optimization Jake B. Perazzone Shiqiang Wang Mingyue Ji Kevin S. Chan FedML 75 0 0 01 Mar 2025
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression Laurent Condat A. Maranjyan Peter Richtárik 47 3 0 07 Mar 2024
Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation Tomàs Ortega Hamid Jafarkhani FedML 30 6 0 01 Aug 2023
FedAgg: Adaptive Federated Learning with Aggregated Gradients Wenhao Yuan Xuehe Wang FedML 48 0 0 28 Mar 2023
Federated Automatic Differentiation Keith Rush Zachary B. Charles Zachary Garrett FedML 34 1 0 18 Jan 2023
Federated Learning with Flexible Control Shiqiang Wang Jake B. Perazzone Mingyue Ji Kevin S. Chan FedML 28 17 0 16 Dec 2022
Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization Heting Liu Fang He Guohong Cao FedML MQ 35 24 0 16 Dec 2022
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Recycling Model Updates in Federated Learning: Are Gradient Subspaces Low-Rank? Sheikh Shams Azam Seyyedali Hosseinalipour Qiang Qiu Christopher G. Brinton FedML 26 20 0 01 Feb 2022
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization Jake B. Perazzone Shiqiang Wang Mingyue Ji Kevin S. Chan FedML 21 72 0 19 Jan 2022
Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory Nicole Mitchell Johannes Ballé Zachary B. Charles Jakub Konecný FedML 13 21 0 07 Jan 2022
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning Irem Ergun Hasin Us Sami Başak Güler FedML 36 26 0 23 Dec 2021
Wyner-Ziv Gradient Compression for Federated Learning Kai Liang Huiru Zhong Haoning Chen Youlong Wu FedML 21 8 0 16 Nov 2021
What Do We Mean by Generalization in Federated Learning? Honglin Yuan Warren Morningstar Lin Ning K. Singhal OOD FedML 41 71 0 27 Oct 2021
Rethinking gradient sparsification as total error minimization Atal Narayan Sahu Aritra Dutta A. Abdelmoniem Trambak Banerjee Marco Canini Panos Kalnis 43 54 0 02 Aug 2021
Federated Learning with Spiking Neural Networks Yeshwanth Venkatesha Youngeun Kim Leandros Tassiulas Priyadarshini Panda FedML 33 47 0 11 Jun 2021
Distributed Learning in Wireless Networks: Recent Progress and Future Challenges Mingzhe Chen Deniz Gündüz Kaibin Huang Walid Saad M. Bennis Aneta Vulgarakis Feljan H. Vincent Poor 38 401 0 05 Apr 2021