Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning

1 code implementation • 1 May 2024 • Seyed Mahmoud Sajjadi Mohammadabadi, Lei Yang, Feng Yan, Junshan Zhang

To minimize training time in heterogeneous environments, we present a Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning (ComDML), which balances the workload among agents through a decentralized approach.

Speed Up Federated Learning in Heterogeneous Environment: A Dynamic Tiering Approach

1 code implementation • 9 Dec 2023 • Seyed Mahmoud Sajjadi Mohammadabadi, Syed Zawad, Feng Yan, Lei Yang

The dynamic tier scheduler assigns clients to suitable tiers to minimize the overall training time in each round.

Federated Learning Image Classification