High-Energy Concentration for Federated Learning in Frequency Domain

• URL: http://arxiv.org/abs/2509.12630v2
• Date: Tue, 28 Oct 2025 01:41:54 GMT
• Title: High-Energy Concentration for Federated Learning in Frequency Domain
• Authors: Haozhi Shi, Weiying Xie, Hangyu Ye, Daixun Li, Jitao Ma, Yunsong Li, Leyuan Fang,
• Abstract summary: We propose a novel Frequency-Domain aware FL method with high-energy concentration (FedFD) to address this problem.<n>FedFD is inspired by the discovery that the discrete cosine transform predominantly distributes energy to specific regions, referred to as high-energy concentration.<n>On five image and speech datasets, FedFD achieves superior performance than state-of-the-art methods while reducing communication costs.
• Score: 62.90597472173119
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) presents significant potential for collaborative optimization without data sharing. Since synthetic data is sent to the server, leveraging the popular concept of dataset distillation, this FL framework protects real data privacy while alleviating data heterogeneity. However, such methods are still challenged by the redundant information and noise in entire spatial-domain designs, which inevitably increases the communication burden. In this paper, we propose a novel Frequency-Domain aware FL method with high-energy concentration (FedFD) to address this problem. Our FedFD is inspired by the discovery that the discrete cosine transform predominantly distributes energy to specific regions, referred to as high-energy concentration. The principle behind FedFD is that low-energy like high-frequency components usually contain redundant information and noise, thus filtering them helps reduce communication costs and optimize performance. Our FedFD is mathematically formulated to preserve the low-frequency components using a binary mask, facilitating an optimal solution through frequency-domain distribution alignment. In particular, real data-driven synthetic classification is imposed into the loss to enhance the quality of the low-frequency components. On five image and speech datasets, FedFD achieves superior performance than state-of-the-art methods while reducing communication costs. For example, on the CIFAR-10 dataset with Dirichlet coefficient $\alpha = 0.01$, FedFD achieves a minimum reduction of 37.78\% in the communication cost, while attaining a 10.88\% performance gain.

Related papers

• Fractional-Order Federated Learning [4.1751058176413105]
  Federated learning (FL) allows remote clients to train a global model collaboratively while protecting client privacy.<n>Despite its privacy-preserving benefits, FL has significant drawbacks, including slow convergence, high communication cost, and non-independent-and-identically-distributed (non-IID) data.
  arXiv  Detail & Related papers  (2026-02-17T06:25:23Z)
• FedCLF -- Towards Efficient Participant Selection for Federated Learning in Heterogeneous IoV Networks [11.632031365965878]
  Federated Learning (FL) is a distributed machine learning technique that preserves data privacy by sharing only the trained parameters instead of the client data.<n>We propose FedCLF with Calibrated Loss and Feedback control, which introduces calibrated loss as a utility in the participant selection process and a feedback control mechanism to dynamically adjust the sampling frequency of the clients.
  arXiv  Detail & Related papers  (2025-09-25T04:51:38Z)
• Improving Noise Efficiency in Privacy-preserving Dataset Distillation [59.57846442477106]
  We introduce a novel framework that decouples sampling from optimization for better convergence and improves signal quality.<n>On CIFAR-10, our method achieves a textbf10.0% improvement with 50 images per class and textbf8.3% increase with just textbfone-fifth the distilled set size of previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-08-03T13:15:52Z)
• FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments [4.358456799125694]
  Pervasive computing environments (e.g., for Human Activity Recognition, HAR) are characterized by resource-constrained end devices, streaming sensor data and intermittent client participation.<n>We propose FlexFed, a novel FL approach that prioritizes data retention for efficient memory use and dynamically adjusts offline training frequency.<n>We also develop a realistic HAR-based evaluation framework that simulates streaming data, dynamic distributions, imbalances and varying availability.
  arXiv  Detail & Related papers  (2025-05-19T14:23:37Z)
• Federated Low-Rank Adaptation with Differential Privacy over Wireless Networks [24.667581521367357]
  Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data.<n>The risk of privacy eavesdropping attacks in FedFT remains a concern, particularly in sensitive areas such as healthcare and finance.<n>We propose a split FedFT framework with differential privacy (DP) over wireless networks.
  arXiv  Detail & Related papers  (2024-11-12T14:01:08Z)
• Towards Resource-Efficient Federated Learning in Industrial IoT for Multivariate Time Series Analysis [50.18156030818883]
  Anomaly and missing data constitute a thorny problem in industrial applications. Deep learning enabled anomaly detection has emerged as a critical direction. The data collected in edge devices contain user privacy.
  arXiv  Detail & Related papers  (2024-11-06T15:38:31Z)
• FedFT: Improving Communication Performance for Federated Learning with Frequency Space Transformation [0.361593752383807]
  We introduce FedFT (federated frequency-space transformation), a simple yet effective methodology for communicating model parameters in a Federated Learning setting. FedFT uses Discrete Cosine Transform (DCT) to represent model parameters in frequency space, enabling efficient compression and reducing communication overhead. We demonstrate the generalisability of the FedFT methodology on four datasets using comparative studies with three state-of-the-art FL baselines.
  arXiv  Detail & Related papers  (2024-09-08T23:05:35Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• FedFM: Anchor-based Feature Matching for Data Heterogeneity in Federated Learning [91.74206675452888]
  We propose a novel method FedFM, which guides each client's features to match shared category-wise anchors. To achieve higher efficiency and flexibility, we propose a FedFM variant, called FedFM-Lite, where clients communicate with server with fewer synchronization times and communication bandwidth costs.
  arXiv  Detail & Related papers  (2022-10-14T08:11:34Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Sparse Federated Learning with Hierarchical Personalized Models [24.763028713043468]
  Federated learning (FL) can achieve privacy-safe and reliable collaborative training without collecting users' private data. We propose a personalized FL algorithm using a hierarchical proximal mapping based on the moreau envelop, named sparse federated learning with hierarchical personalized models (sFedHP) A continuously differentiable approximated L1-norm is also used as the sparse constraint to reduce the communication cost.
  arXiv  Detail & Related papers  (2022-03-25T09:06:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.