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Task-oriented Framework for Communication-Efficient Federated Learning: From Isolated Optimization to Holistic Synergy

arXiv 2609.04945 ↗First posted 4 Sep 2026eess.SPcited by 0

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Abstract (arXiv)

Communication bottlenecks remain a primary obstacle to the large-scale deployment of federated learning (FL). This article proposes a comprehensive framework for building communication-efficient FL, founded on three fundamental pillars: model compression, client selection, and resource allocation. We first survey state-of-the-art techniques for each pillar, specifically elucidating how quantization, pruning, and low-rank approximation reduce payloads; how intelligent client schedulers exploit heterogeneity; and how emerging communication paradigms such as Integrated Sensing and Communication (ISAC) and Over-the-Air Computation (AirComp) redefine bandwidth and energy utilization. Subsequently, these insights are unified through a task-oriented design philosophy that couples strategy selection with cross-layer, multi-objective optimization. To validate the proposed framework, we present an autonomous driving case study with two complementary experiments: a task-oriented client scheduling strategy that improves object detection accuracy under the same communication time budget, and a joint quantization-bandwidth optimization that further reduces total training time under dynamic networks. Together, the experiments demonstrate the advantages of holistic task-oriented design for real-world FL deployment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 15 references that are in the map. First posted on arXiv 4 Sep 2026.

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8 of 15 references.

References · 8

  1. 2019Model Pruning Enables Efficient Federated Learning on Edge Devicescited by 222
  2. 2021FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learningcited by 141
  3. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
  4. 2021Optimized Power Control Design for Over-the-Air Federated Edge Learningcited by 48
  5. 2024Advances and Open Challenges in Federated Foundation Modelscited by 36
  6. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 31
  7. 2023Greedy Shapley Client Selection for Communication-Efficient Federated Learningcited by 7
  8. 2024Joint Communication and Over-the-Air Computation for Semi-Federated Learning Towards Scalable AI in Computing-Heterogeneous IoT Systemscited by 2

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  1. 2025Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
  2. 2024Energy-Efficient Wireless Federated Learning via Doubly Adaptive Quantization
  3. 2024FedComLoc: Communication-Efficient Distributed Training of Sparse and Quantized Models
  4. 2022Communication-Efficient Federated Learning With Data and Client Heterogeneity
  5. 2022Resource Allocation for Compression-aided Federated Learning with High Distortion Rate
  6. 2022Communication-Efficient Adaptive Federated Learning
  7. 2024FedFQ: Federated Learning with Fine-Grained Quantization
  8. 2026Quantization in Federated Learning: Methods, Challenges and Future Directions