167 citations · 339 across the 26 of their papers we have counts for
28 papers
Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels
Yae Jee Cho, Gauri Joshi, Dimitrios Dimitriadis
Many existing FL methods assume clients with fully-labeled data, while in realistic settings, clients have limited labels due to the expensive and laborious process of labeling. Li…
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond
Jiin Woo, Gauri Joshi, Yuejie Chi
When the data used for reinforcement learning (RL) are collected by multiple agents in a distributed manner, federated versions of RL algorithms allow collaborative learning withou…
Federated Minimax Optimization with Client Heterogeneity
Pranay Sharma, Rohan Panda, Gauri Joshi
Minimax optimization has seen a surge in interest with the advent of modern applications such as GANs, and it is inherently more challenging than simple minimization. The difficult…
On the Convergence of Federated Averaging with Cyclic Client Participation
Yae Jee Cho, Pranay Sharma, Gauri Joshi +3
Federated Averaging (FedAvg) and its variants are the most popular optimization algorithms in federated learning (FL). Previous convergence analyses of FedAvg either assume full cl…
FedExP: Speeding Up Federated Averaging via Extrapolation
Divyansh Jhunjhunwala, Shiqiang Wang, Gauri Joshi
Federated Averaging (FedAvg) remains the most popular algorithm for Federated Learning (FL) optimization due to its simple implementation, stateless nature, and privacy guarantees…
FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning
Divyansh Jhunjhunwala, Pranay Sharma, Aushim Nagarkatti +1
Data-heterogeneous federated learning (FL) systems suffer from two significant sources of convergence error: 1) client drift error caused by performing multiple local optimization…