Curse or Redemption? How Data Heterogeneity Affects the Robustness of
Federated Learning

Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning

1 February 2021

Ali Anwar

Nathalie Baracaldo

Papers citing "Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning"

6 / 6 papers shown

Title
Decoding FL Defenses: Systemization, Pitfalls, and Remedies M. A. Khan Virat Shejwalkar Yasra Chandio Amir Houmansadr Fatima M. Anwar AAML 38 0 0 03 Feb 2025
Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning Naveen Kumar Krishna Mohan Aravind Machiry AAML 42 1 0 05 Apr 2024
Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks Zeyu Qin Liuyi Yao Daoyuan Chen Yaliang Li Bolin Ding Minhao Cheng FedML 38 25 0 03 Feb 2023
Distributed Distributionally Robust Optimization with Non-Convex Objectives Yang Jiao Kai Yang Dongjin Song 29 11 0 14 Oct 2022
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning Shaoxiong Ji Yue Tan Teemu Saravirta Zhiqin Yang Yixin Liu Lauri Vasankari Shirui Pan Guodong Long A. Walid FedML 37 76 0 25 Feb 2021
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 191 1,033 0 29 Nov 2018