mlsanityData to 14 Oct 2026

Hardware-Aware Federated Learning for Speech Emotion Recognition

arXiv 2605.24712 ↗First posted 23 May 2026cs.LG cs.HCcited by 0

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Abstract (arXiv)

Federated learning (FL) enables privacy-preserving collaborative training across distributed edge devices, but real deployments involve heterogeneous clients with different processing power, memory capacity, and communication latency, which often increase round duration and system cost. This paper proposes a hardware-aware federated learning framework for emotion recognition on session-partitioned IEMOCAP that integrates hardware profiling, top-K client selection, and adaptive local epochs within a unified training loop. We compare the method against FedAvg, FedProx, and random top-K selection under a non-IID setup and show that, across 50 federated rounds and 5 independent trials, the proposed approach achieves competitive validation accuracy (0.352), reduces total training time by about 36.5% compared to FedAvg, and lowers cumulative communication cost by 40%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 18 references that are in the map. First posted on arXiv 23 May 2026.

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10 of 18 references.

References · 10

  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. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2019Towards Federated Learning at Scale: System Designcited by 832
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  6. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 352
  7. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
  8. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  9. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  10. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39

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