Hardware-Aware Federated Learning for Speech Emotion Recognition
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Machine Learning
Title:Hardware-Aware Federated Learning for Speech Emotion Recognition
Abstract: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%.
| Comments: | 4 pages, 3 figures, 4 Tables |
| Subjects: | Machine Learning (cs.LG); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2605.24712 [cs.LG] |
| (or arXiv:2605.24712v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24712
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Beyazit Bestami Yuksel [view email][v1] Sat, 23 May 2026 19:52:38 UTC (874 KB)
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
More from arXiv — Machine Learning
-
Algometrics: Forecasting Under Algorithmic Feedback
May 26
-
Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection
May 26
-
CAFD: Concept-Aware DNN Fault Detection using VLMs
May 26
-
Towards Verifiable Transformers: Solver-Checkable Circuit Explanations
May 26
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.