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Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning

arXiv 2609.39250 ↗First posted 30 Sep 2026cs.LG cs.DCcited by 0

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Abstract (arXiv)

Federated learning (FL) is a promising paradigm of machine learning, which preserves user privacy by enabling learning without sharing raw data with a cloud server. Straggling clients have been a problem for FL as they introduce delays in aggregating the local models and hence, the convergence of the global model. Therefore, it is important to have a mechanism that ensures fast convergence of the global model as well as good FL participation rate. Another issue for the convergence of a model in FL is the non-independent and identically distributed (non-iid) data across the clients. Prior approaches based on probabilistic client selection do not work well under non-iid data especially when the number of clients is small. We show scenarios where such approaches fail and propose a joint client-training data selection algorithm for fast convergence of FL models. Our experiments on CIFAR-100 dataset show that convergence of the FL model can be significantly improved over prior works that can consider non-iid data and heterogeneous computation and higher model accuracy.

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References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 851
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  7. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  8. 2020Multi-Armed Bandit Based Client Scheduling for Federated Learningcited by 57
  9. 2020Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneitycited by 56
  10. 2019Device Scheduling with Fast Convergence for Wireless Federated Learningcited by 46
  11. 2020Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneitycited by 20
  12. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 20
6 more
  1. 2020Federated Learning under Channel Uncertainty: Joint Client Scheduling and Resource Allocationcited by 18
  2. 2021Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learningcited by 10
  3. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  4. 2019From Server-Based to Client-Based Machine Learning: A Comprehensive Surveycited by 7
  5. 2020Optimal Task Assignment to Heterogeneous Federated Learning Devicescited by 7
  6. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1

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