FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning

Hossein Fereidooni

Network and Distributed System Security (NDSS) Symposium 2024 · Day 1 · Poisoning Attacks

Federated Learning (FL) has emerged as a crucial paradigm for collaborative machine learning, enabling multiple clients to jointly train a global model without sharing their sensitive local data. This decentralized approach offers significant privacy benefits, making it highly attractive for applications in healthcare, finance, and mobile computing. However, this distributed nature also introduces a critical vulnerability: **poisoning attacks**. These attacks, whether untargeted (aiming to degrade overall model performance) or targeted (implanting hidden backdoors), pose a severe threat to the integrity and reliability of FL models, often going unnoticed for extended periods and leading to serious security breaches.