Robust and Communication-Efficient Federated Learning From Non-i.i.d. Data

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 9; pp. 3400 - 3413
• Main Authors: Sattler, Felix, Wiedemann, Simon, Muller, Klaus-Robert, Samek, Wojciech
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cognitive tasks; Communication; Compression; Data models; Deep learning; Distributed databases; distributed learning; efficient communication; Federated learning; Machine learning; Optimization; privacy-preserving machine learning; Protocols; Servers; Training; Training data

Federated learning allows multiple parties to jointly train a deep learning model on their combined data, without any of the participants having to reveal their local data to a centralized server. This form of privacy-preserving collaborative learning, however, comes at the cost of a significant com...