Yuejie Chi

Resource-Efficient Decentralized and Federated Learning

Distributed optimization has been a classic topic, yet is attracting significant interest recently in machine learning due to its numerous applications such as distributed training, multi-agent learning, federated optimization, and so on. Often, the scale of modern datasets has exceeded the capacity of a single machine, and privacy and communication constraints prevent information sharing in a centralized manner and necessitates distributed infrastructures. Broadly speaking, there are two types of distributed settings: a distributed/federated setting, where a parameter server aggregates and shares parameters across all agents; and a decentralized/network setting, where each agent only aggregates and shares parameters with its neighbors over a network topology. The canonical problem of empirical risk minimization in the distributed setting leads to intriguing trade-offs between computation and communication that are not well understood; moreover, data unbalancedness and heterogeneity across agents poses additional challenges in both algorithmic convergence and statistical efficacy, often exacerbated by additional bandwidth and privacy constraints.

Overview

• Advances in Federated Optimization: Efficiency, Resiliency, and Privacy
  Y. Chi and Z. Li, ICASSP tutorial, 2023.

Federated and Decentralized Learning with Communication Compression and Differential Privacy

• Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression [Arxiv]
  S. Chen, Z. Li, and Y. Chi, International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

• Convergence and Privacy of Decentralized Nonconvex Optimization with Gradient Clipping and Communication Compression [Arxiv]
  B. Li and Y. Chi, preprint.

• SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression [Arxiv] [Code]
  Z. Li, H. Zhao, B. Li, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2022.

• BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compression [Arxiv] [Code]
  H. Zhao, B. Li, Z. Li, P. Richtarik, and Y. Chi, Conference on Neural Information Processing Systems (NeurIPS), 2022.

Communication-Efficient Federated Learning and Optimization

• Communication-Efficient Federated Optimization over Semi-Decentralized Networks [Arxiv]
  H. Wang and Y. Chi, preprint. Short version at ICASSP 2024.

• A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning [Arxiv]
  P. Valdeira, Y. Chi, C. Soares, and J. Xavier, preprint.

• DESTRESS: Computation-Optimal and Communication-Efficient Decentralized Nonconvex Finite-Sum Optimization [Arxiv] [Code]
  B. Li, Z. Li, and Y. Chi, SIAM Journal on Mathematics of Data Science, vol. 4, no. 3, pp. 1031-1051, 2022. Short version at OPT 2021 as a spotlight presentation.

• Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance Reduction [Arxiv] [Code]
  B. Li, S. Cen, Y. Chen, and Y. Chi, Journal of Machine Learning Research, vol. 21, no. 180, pp. 1-51, 2020. Short version at AISTATS 2019.

• Convergence of Distributed Stochastic Variance Reduced Methods without Sampling Extra Data [Arxiv]
  S. Cen, H. Zhang, Y. Chi, W. Chen and T.-Y. Liu, IEEE Trans. on Signal Processing, vol. 68, pp. 3976-3989, 2020.

Federated Reinforcement Learning

• Federated Offline Reinforcement Learning: Collaborative Single-Policy Coverage Suffices [Arxiv]
  J. Woo, L. Shi, G. Joshi, and Y. Chi, preprint.

• Federated Natural Policy Gradient Methods for Multi-task Reinforcement Learning [Arxiv]
  T. Yang, S. Cen, Y. Wei, Y. Chen, and Y. Chi, preprint.

• The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond [Arxiv]
  J. Woo, G. Joshi, and Y. Chi, preprint. Short version at ICML 2023.