mlsanityData to 14 Oct 2026

RIFLES: Resource-effIcient Federated LEarning via Scheduling

arXiv 2505.13169 ↗First posted 19 May 2025cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) is a privacy-preserving machine learning technique that allows decentralized collaborative model training across a set of distributed clients, by avoiding raw data exchange. A fundamental component of FL is the selection of a subset of clients in each round for model training by a central server. Current selection strategies are myopic in nature in that they are based on past or current interactions, often leading to inefficiency issues such as straggling clients. In this paper, we address this serious shortcoming by proposing the RIFLES approach that builds a novel availability forecasting layer to support the client selection process. We make the following contributions: (i) we formalise the sequential selection problem and reduce it to a scheduling problem and show that the problem is NP-complete, (ii) leveraging heartbeat messages from clients, RIFLES build an availability prediction layer to support (long term) selection decisions, (iii) we propose a novel adaptive selection strategy to support efficient learning and resource usage. To circumvent the inherent exponential complexity, we present RIFLES, a heuristic that leverages clients' historical availability data by using a CNN-LSTM time series forecasting model, allowing the server to predict the optimal participation times of clients, thereby enabling informed selection decisions. By comparing against other FL techniques, we show that RIFLES provide significant improvement by between 10%-50% on a variety of metrics such as accuracy and test loss. To the best of our knowledge, it is the first work to investigate FL as a scheduling problem.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 19 May 2025.

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19 of 36 references.

References · 19

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2020Adaptive Federated Optimizationcited by 1,046
  5. 2019Towards Federated Learning at Scale: System Designcited by 832
  6. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  7. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  8. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 198
  9. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 190
  10. 2021Federated Learning for Open Bankingcited by 141
  11. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 136
  12. 2020Optimal Client Sampling for Federated Learningcited by 124
7 more
  1. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
  2. 2019SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overheadcited by 82
  3. 2020Towards Flexible Device Participation in Federated Learningcited by 65
  4. 2022Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraintscited by 30
  5. 2020Dynamic Clustering in Federated Learningcited by 27
  6. 2021Resource-Efficient Federated Learningcited by 26
  7. 2020Robust Aggregation for Adaptive Privacy Preserving Federated Learning in Healthcarecited by 18

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