摘要
Federated Learning(FL)is a machine learning paradigm where multiple data owners collaboratively train a model under the coordination of a central server,while keeping all data decentralized.Such a paradigm allows models to be trained effectively while avoiding data privacy leakage.However,federated learning is vulnerable to various kinds of failures as a result of both intentional(malicious)and none intentional(non-malicious)attacks.Existing studies on attacks in federated learning are mostly dedicated to the automatic defense against malicious attacks(e.g.,data poisoning attacks).Relatively,less attention has been given to handling nonmalicious failures(e.g.,non-independent and identically distributed data failures),which are actually more common and difficult-to-handle in a federated learning setting.In this paper,we propose FedCare,a real-time visual diagnosis approach for handling failures in federated learning systems.The functionality of FedCare includes the identification of failures,the assessment of their nature(malicious or non-malicious),the study of their impact,and the recommendation of adequate defense strategies.Our design is multi-faceted,giving perspectives from the angles of model performance,anomaly/contribution assessment of clients,features maps,group activities,and client impact.We demonstrate the effectiveness of our approach through two case studies,a quantitative experiment and an expert interview.
基金
supported by the National Natural Science Foundation of China(Grant Nos.62132017,61772456,61761136020)
the Zhejiang Provincial Natural Science Foundation of China(LD24F020011)
the“Pioneer and Leading Goose”R&D Program of Zhejiang(2024C01167).
