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

17 June 2022

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

7 / 7 papers shown

Title
Federated Adapter on Foundation Models: An Out-Of-Distribution Approach Yiyuan Yang Guodong Long Dinesh Manocha Qinghua Lu Shanshan Ye Jing Jiang OODD 258 1 0 02 May 2025
Tackling Feature and Sample Heterogeneity in Decentralized Multi-Task Learning: A Sheaf-Theoretic Approach Chaouki Ben Issaid Praneeth Vepakomma Mehdi Bennis 84 0 0 03 Feb 2025
Scalable and Resource-Efficient Second-Order Federated Learning via Over-the-Air Aggregation Abdulmomen Ghalkha Chaouki Ben Issaid Mehdi Bennis 29 0 0 10 Oct 2024
Federated Combinatorial Multi-Agent Multi-Armed Bandits Fares Fourati Mohamed-Slim Alouini Vaneet Aggarwal FedML 33 5 0 09 May 2024
Rendering Wireless Environments Useful for Gradient Estimators: A Zero-Order Stochastic Federated Learning Method Elissa Mhanna Mohamad Assaad 55 1 0 30 Jan 2024
Heterogeneous Federated Learning: State-of-the-art and Research Challenges Mang Ye Xiuwen Fang Bo Du PongChi Yuen Dacheng Tao FedML AAML 44 250 0 20 Jul 2023
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing Nicolò Dal Fabbro S. Dey M. Rossi Luca Schenato FedML 36 14 0 11 Feb 2022