Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning

5 May 2020

Papers citing "Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning"

7 / 7 papers shown

Title
Generalization in Federated Learning: A Conditional Mutual Information Framework Ziqiao Wang Cheng Long Yongyi Mao FedML 99 1 0 06 Mar 2025
Heterogeneity Matters even More in Distributed Learning: Study from Generalization Perspective Masoud Kavian Romain Chor Milad Sefidgaran Abdellatif Zaidi FedML 114 1 0 03 Mar 2025
Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization Wenrui Yu Qiongxiu Li Milan Lopuhaä-Zwakenberg Mads Græsbøll Christensen Richard Heusdens FedML 85 4 0 12 Jul 2024
Quantifying the Impact of Label Noise on Federated Learning Shuqi Ke Chao Huang Xin Liu FedML 114 7 0 15 Nov 2022
What Do We Mean by Generalization in Federated Learning? Honglin Yuan Warren Morningstar Lin Ning K. Singhal OOD FedML 123 76 0 27 Oct 2021
An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints Sharu Theresa Jose Osvaldo Simeone FedML 60 0 0 11 Oct 2021
Generalization Bounds for Noisy Iterative Algorithms Using Properties of Additive Noise Channels Hao Wang Rui Gao Flavio du Pin Calmon 81 18 0 05 Feb 2021