mlsanityData to 14 Oct 2026

A SER-based Device Selection Mechanism in Multi-bits Quantization Federated Learning

arXiv 2405.02320 ↗First posted 20 Apr 2024cs.IT cs.AI math.ITcited by 0

Not on the map. Why?

Abstract (arXiv)

The quality of wireless communication will directly affect the performance of federated learning (FL), so this paper analyze the influence of wireless communication on FL through symbol error rate (SER). In FL system, non-orthogonal multiple access (NOMA) can be used as the basic communication framework to reduce the communication congestion and interference caused by multiple users, which takes advantage of the superposition characteristics of wireless channels. The Minimum Mean Square Error (MMSE) based serial interference cancellation (SIC) technology is used to recover the gradient of each terminal node one by one at the receiving end. In this paper, the gradient parameters are quantized into multiple bits to retain more gradient information to the maximum extent and to improve the tolerance of transmission errors. On this basis, we designed the SER-based device selection mechanism (SER-DSM) to ensure that the learning performance is not affected by users with bad communication conditions, while accommodating as many users as possible to participate in the learning process, which is inclusive to a certain extent. The experiments show the influence of multi-bit quantization of gradient on FL and the necessity and superiority of the proposed SER-based device selection mechanism.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 10 references that are in the map. First posted on arXiv 20 Apr 2024.

Builds on

5 of 10 references.

References · 5

  1. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  2. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  3. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  4. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  5. 2020Reconfigurable Intelligent Surface Enabled Federated Learning: A Unified Communication-Learning Design Approachcited by 33

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2022Performance Optimization for Variable Bitwidth Federated Learning in Wireless Networks
  2. 2022Wireless Quantized Federated Learning: A Joint Computation and Communication Design
  3. 2021Quantized Federated Learning under Transmission Delay and Outage Constraints
  4. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions
  5. 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
  6. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  7. 2024FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization
  8. 2022Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing