Semi-decentralized Federated Time Series Prediction with Client Availability Budgets
This addresses client availability issues in federated learning for IoT scenarios, but it is incremental as it builds on existing FL methods with a novel selection approach.
The paper tackled the problem of client selection in federated learning with time-series data under constraints like limited energy and availability, proposing FedDeCAB, a semi-decentralized method that improved performance by allowing partial model parameter sharing from nearest neighbors when clients are offline, achieving effectiveness on real-world taxi and vessel trajectory datasets.
Federated learning (FL) effectively promotes collaborative training among distributed clients with privacy considerations in the Internet of Things (IoT) scenarios. Despite of data heterogeneity, FL clients may also be constrained by limited energy and availability budgets. Therefore, effective selection of clients participating in training is of vital importance for the convergence of the global model and the balance of client contributions. In this paper, we discuss the performance impact of client availability with time-series data on federated learning. We set up three different scenarios that affect the availability of time-series data and propose FedDeCAB, a novel, semi-decentralized client selection method applying probabilistic rankings of available clients. When a client is disconnected from the server, FedDeCAB allows obtaining partial model parameters from the nearest neighbor clients for joint optimization, improving the performance of offline models and reducing communication overhead. Experiments based on real-world large-scale taxi and vessel trajectory datasets show that FedDeCAB is effective under highly heterogeneous data distribution, limited communication budget, and dynamic client offline or rejoining.