Anis Elgabli — accepted AI-conference papers

2022

FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning

Anis Elgabli, Chaouki Ben Issaid, Amrit Singh Bedi, Ketan Rajawat, Mehdi Bennis, Vaneet Aggarwal

ICML 2022spotlight

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 due to the requirement of sending Hessian information from clients to parameter server (PS). In this work, we introduced…

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2020

Q-GADMM: Quantized Group ADMM for Communication Efficient Decentralized Machine Learning

Anis Elgabli, Jihong Park, Amrit S. Bedi, Mehdi Bennis, Vaneet Aggarwal

ICASSP 2020accepted

In this paper, we propose a communication-efficient decen-tralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct method of multiplier (GADMM), thereby ensuri…

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