Performance Weighting for Robust Federated Learning Against Corrupted Sources
Federated Learning has emerged as a dominant computational paradigm for distributed machine learning. Its unique data privacy properties allow us to collaboratively train models while offering participating clients certain privacy-preserving guarantees. However, in real-world applications, a federated environment may consist of a mixture of benevolent and malicious clients, with the latter aiming to corrupt and degrade federated model's performance. Different corruption schemes may be applied such as model poisoning and data corruption. Here, we focus on the latter, the susceptibility of federated learning to various data corruption attacks. We show that the standard global aggregation scheme of local weights is inefficient in the presence of corrupted clients. To mitigate this problem, we propose a class of task-oriented performance-based methods computed over a distributed validation dataset with the goal to detect and mitigate corrupted clients. Specifically, we construct a robust weight aggregation scheme based on geometric mean and demonstrate its effectiveness under random label shuffling and targeted label flipping attacks.
Code (0)
등록된 구현이 없습니다.
Tasks
Federated LearningModel PoisoningPrivacy PreservingSimilar Papers 제목 키워드 기반
Auto-weighted Robust Federated Learning with Corrupted Data Sources
Federated learning provides a communication-efficient and privacy-preserving training process by enabling learning statistical models with massive participants while keeping their data in local clients. However, standard…
Federated LearningPrivacy PreservingRobust Federated Training via Collaborative Machine Teaching using Trusted Instances
Federated learning performs distributed model training using local data hosted by agents. It shares only model parameter updates for iterative aggregation at the server. Although it is privacy-preserving by design, feder…
Data PoisoningFederated LearningPrivacy PreservingFedStaleWeight: Buffered Asynchronous Federated Learning with Fair Aggregation via Staleness Reweighting
Federated Learning (FL) endeavors to harness decentralized data while preserving privacy, facing challenges of performance, scalability, and collaboration. Asynchronous Federated Learning (AFL) methods have emerged as pr…
FairnessFederated LearningHierarchical Over-the-Air FedGradNorm
Multi-task learning (MTL) is a learning paradigm to learn multiple related tasks simultaneously with a single shared network where each task has a distinct personalized header network for fine-tuning. MTL can be integrat…
Federated LearningMulti-Task LearningPersonalized Federated LearningFedDefender: Client-Side Attack-Tolerant Federated Learning
Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfer…
Federated LearningKnowledge DistillationModel Poisoning