111 citations · 338 across the 50 of their papers we have counts for
4 papers · 1 filter
Optimized Gradient Tracking for Decentralized Online Learning
Shivangi Dubey Sharma, Ketan Rajawat
This work considers the problem of decentralized online learning, where the goal is to track the optimum of the sum of time-varying functions, distributed across several nodes in a…
FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning
Anis Elgabli, Chaouki Ben Issaid, Amrit S. Bedi +3
Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy d…
STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning
Prashant Khanduri, Pranay Sharma, Haibo Yang +4
Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL pr…
Stochastic Compositional Gradient Descent under Compositional Constraints
Srujan Teja Thomdapu, Harshvardhan, Ketan Rajawat
This work studies constrained stochastic optimization problems where the objective and constraint functions are convex and expressed as compositions of stochastic functions. The pr…