6 papers
Non-Convex Federated Optimization under Cost-Aware Client Selection
Xiaowen Jiang, Anton Rodomanov, Sebastian U. Stich
Different federated optimization algorithms typically employ distinct client-selection strategies: some methods communicate only with a randomly sampled subset of clients at each r…
Composite Optimization with Error Feedback: the Dual Averaging Approach
Yuan Gao, Anton Rodomanov, Jeremy Rack +1
Communication efficiency is a central challenge in distributed machine learning training, and message compression is a widely used solution. However, standard Error Feedback (EF) m…
FedMuon: Federated Learning with Bias-corrected LMO-based Optimization
Yuki Takezawa, Anastasia Koloskova, Xiaowen Jiang +1
Recently, a new optimization method based on the linear minimization oracle (LMO), called Muon, has been attracting increasing attention since it can train neural networks faster t…
Exploiting Similarity for Computation and Communication-Efficient Decentralized Optimization
Yuki Takezawa, Xiaowen Jiang, Anton Rodomanov +1
Reducing communication complexity is critical for efficient decentralized optimization. The proximal decentralized optimization (PDO) framework is particularly appealing, as method…
Accelerated Distributed Optimization with Compression and Error Feedback
Yuan Gao, Anton Rodomanov, Jeremy Rack +1
Modern machine learning tasks often involve massive datasets and models, necessitating distributed optimization algorithms with reduced communication overhead. Communication compre…
Decoupled SGDA for Games with Intermittent Strategy Communication
Ali Zindari, Parham Yazdkhasti, Anton Rodomanov +2
We focus on reducing communication overhead in multiplayer games, where frequently exchanging strategies between players is not feasible and players have noisy or outdated strategi…