Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis

no code implementations • 9 Apr 2024 • Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi, Vaneet Aggarwal, Christopher G. Brinton

Moreover, compared to synchronous FedPG, AFedPG improves the time complexity from $\mathcal{O}(\frac{t_{\max}}{N})$ to $\mathcal{O}(\frac{1}{\sum_{i=1}^{N} \frac{1}{t_{i}}})$, where $t_{i}$ denotes the time consumption in each iteration at the agent $i$, and $t_{\max}$ is the largest one.

Communication Efficient and Privacy-Preserving Federated Learning Based on Evolution Strategies

1 code implementation • 5 Nov 2023 • Guangchen Lan

Federated learning (FL) is an emerging paradigm for training deep neural networks (DNNs) in distributed manners.

Federated Learning Privacy Preserving