3 accepted papers
Federated Learning (FL) has become the de facto standard for privacy-preserving intelligence, largely due to Secure Aggregation protocols that guarantee the mathematical invisibility of individual user contributions. However, we contend that this pursuit of perfect privacy has engineered a systemic …
Gradient sparsification, while mitigating communication bottlenecks in Federated Learning (FL), fundamentally alters the geometric landscape of model updates. We reveal that the resultant high-dimensional orthogonality renders traditional Euclidean-based robust aggregation metrics mathematically amb
Federated learning (FL) is inherently susceptible to privacy breaches and poisoning attacks. To tackle these challenges, researchers have separately devised secure aggregation mechanisms to protect data privacy and robust aggregation methods that withstand poisoning attacks. However, simultaneously…