Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning
||Device Scheduling and Update Aggregation Policies for Asynchronous Federated Learning|
||SNIC Small Compute|
||Chung-Hsuan Hu <firstname.lastname@example.org>|
||2021-04-16 – 2022-05-01|
Federated learning (FL) is a decentralized collaborative learning framework with multiple agents participating in a common training process iteratively over locally distributed data. There are common issues in FL systems such as stringent communication costs in wireless communication. In this work, we propose several scheduling and aggregation policies for FL system to resolve the issue and guarantee convergence of the system.