New Paper: Federated Learning Under Data Heterogeneity

We are excited to share our latest paper accepted at IMCOM 2025: Towards Robust Federated Learning: Investigating Poisoning Attacks Under Clients Data Heterogeneity.

Federated learning enables collaborative model training across distributed clients without sharing raw data — but it remains vulnerable to poisoning attacks, especially when client data distributions are heterogeneous.

Our work systematically evaluates state-of-the-art poisoning attack strategies under realistic non-IID data settings. We analyze how data heterogeneity affects attack success rates and propose defenses tailored to this challenging scenario.

Key contributions:

• Comprehensive evaluation of poisoning attacks under varying degrees of data heterogeneity
• Analysis of why standard defenses (FedAvg + median/trimmed-mean aggregation) fail in non-IID settings
• Lightweight mitigation strategy that degrades attack success without sacrificing model accuracy

The full paper is available on our publications page.