Value of Information and Timing-aware Scheduling for Federated Learning
This work addresses the challenge of efficient FL implementation for privacy-preserving AI, but it is incremental as it builds on existing scheduling methods with minor enhancements.
The paper tackles the problem of scheduling in Federated Learning (FL) under communication constraints by proposing a policy that combines Age of Update (AoU) and data Shapley metrics to consider update freshness, data value, and channel conditions, with effectiveness demonstrated through simulations.
Data possesses significant value as it fuels advancements in AI. However, protecting the privacy of the data generated by end-user devices has become crucial. Federated Learning (FL) offers a solution by preserving data privacy during training. FL brings the model directly to User Equipments (UEs) for local training by an access point (AP). The AP periodically aggregates trained parameters from UEs, enhancing the model and sending it back to them. However, due to communication constraints, only a subset of UEs can update parameters during each global aggregation. Consequently, developing innovative scheduling algorithms is vital to enable complete FL implementation and enhance FL convergence. In this paper, we present a scheduling policy combining Age of Update (AoU) concepts and data Shapley metrics. This policy considers the freshness and value of received parameter updates from individual data sources and real-time channel conditions to enhance FL's operational efficiency. The proposed algorithm is simple, and its effectiveness is demonstrated through simulations.