mlsanityData to 14 Oct 2026

Semi-decentralized Federated Time Series Prediction with Client Availability Budgets

arXiv 2509.03660 ↗First posted 3 Sep 2025cs.LG cs.DCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 3 Sep 2025.

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19 of 31 references.

References · 19

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  10. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  11. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  12. 2023Federated Learning under Heterogeneous and Correlated Client Availabilitycited by 18
7 more
  1. 2022Semi-Decentralized Federated Learning with Collaborative Relayingcited by 16
  2. 2018A Multi-task Deep Learning Architecture for Maritime Surveillance using AIS Data Streamscited by 13
  3. 2021NetTraj: A Network-based Vehicle Trajectory Prediction Model with Directional Representation and Spatiotemporal Attention Mechanismscited by 13
  4. 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11
  5. 2022Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learningcited by 11
  6. 2022FedTADBench: Federated Time-Series Anomaly Detection Benchmarkcited by 6
  7. 2023FedLE: Federated Learning Client Selection with Lifespan Extension for Edge IoT Networkscited by 3

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