Privacy-Preserving Efficient Federated-Learning Model Debugging

• Published in: IEEE transactions on parallel and distributed systems Vol. 33; no. 10; pp. 2291 - 2303
• Main Authors: Li, Anran, Zhang, Lan, Wang, Junhao, Han, Feng, Li, Xiang-Yang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Clients; Computational modeling; Data models; data quality assessment; Debugging; Federated learning; Influence function; Laptop computers; Machine learning; Model testing; Predictive models; Privacy; Training; Training data; Visibility

Federated learning allows large amounts of mobile clients to jointly construct a global model without sending their private data to a central server. A fundamental issue in this framework is the susceptibility to the erroneous training data. This problem is especially challenging due to the invisibi...