Robust asynchronous federated learning with time-weighted and stale model aggregation

• Main Authors: MIAO, Yinbin, LIU, Ziteng, LI, Xinghua, LI, Meng, LI, Hongwei, CHOO, Kim-Kwang Raymond, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Computational modelling; Convergence; Federated learning; Heterogeneity; Homomorphic encryptions; Homomorphic-encryptions; Lightweight computing; Privacy; Symmetric homomorphic encryption; Symmetrics; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/9677; https://ink.library.smu.edu.sg/context/sis_research/article/10677/viewcontent/Robust_Asynchronous_Federated_Learning_With_Time_Weighted_and_Stale_Model_Aggregation.pdf
• Institution: Singapore Management University

Federated Learning (FL) ensures collaborative learning among multiple clients while maintaining data locally. However, the traditional synchronous FL solutions have lower accuracy and require more communication time in scenarios where most devices drop out during learning. Therefore, we propose an A...