Effective Edge Server Placement for Efficient Federated Clustering
Sungwoong Yeom, Shivani Sanjay Kolekar, Kyungbaek Kim · 2022 23rd Asia-Pacific Network Operations and Management Symposium (APNOMS) · 2022
Recently, research on federated clustering has been actively studied to improve the performance of federated learning to solve the non-i.i.d issue. Federated clustering makes clusters with members who has similar characteristics of data which is used as inputs of federated learning, and each cluster trains an artificial intelligence model in a federated manner. However, if distances between members of a cluster configured through federated clustering is long in a network, the overhead related to federated learning becomes larger than expected and it may be lose the network cost benefits of federated learning. In this paper, we propose a DTW(Dynamic Time Warping) based federated clustering and MIP(Mixed Integer Programming)-based edge server placement in order to reduce the network overhead of federated learning caused by federated clustering under non-i.i.d setting.