FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices

• URL: http://arxiv.org/abs/2601.17713v1
• Date: Sun, 25 Jan 2026 06:01:19 GMT
• Title: FedCCA: Client-Centric Adaptation against Data Heterogeneity in Federated Learning on IoT Devices
• Authors: Kaile Wang, Jiannong Cao, Yu Yang, Xiaoyin Li, Yinfeng Cao,
• Abstract summary: Client-Centric Adaptation federated learning (FedCCA) is an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client.<n>We conduct extensive experiments on diverse datasets to assess the efficacy of FedCCA.
• Score: 16.902104043318975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid development of the Internet of Things (IoT), AI model training on private data such as human sensing data is highly desired. Federated learning (FL) has emerged as a privacy-preserving distributed training framework for this purpuse. However, the data heterogeneity issue among IoT devices can significantly degrade the model performance and convergence speed in FL. Existing approaches limit in fixed client selection and aggregation on cloud server, making the privacy-preserving extraction of client-specific information during local training challenging. To this end, we propose Client-Centric Adaptation federated learning (FedCCA), an algorithm that optimally utilizes client-specific knowledge to learn a unique model for each client through selective adaptation, aiming to alleviate the influence of data heterogeneity. Specifically, FedCCA employs dynamic client selection and adaptive aggregation based on the additional client-specific encoder. To enhance multi-source knowledge transfer, we adopt an attention-based global aggregation strategy. We conducted extensive experiments on diverse datasets to assess the efficacy of FedCCA. The experimental results demonstrate that our approach exhibits a substantial performance advantage over competing baselines in addressing this specific problem.

Related papers

• FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors [50.131271229165165]
  Federated Learning (FL) has emerged as a promising framework for distributed machine learning.<n>Data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning.<n>We propose Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process.
  arXiv  Detail & Related papers  (2025-03-20T04:49:40Z)
• HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast [10.652998357266934]
  We propose a system heterogeneous federation method based on data-free knowledge distillation and two-way contrast (HFedCKD)<n>HFedCKD effectively alleviates the knowledge offset caused by a low participation rate under data-free knowledge distillation and improves the performance and stability of the model.<n>We conduct extensive experiments on image and IoT datasets to comprehensively evaluate and verify the generalization and robustness of the proposed HFedCKD framework.
  arXiv  Detail & Related papers  (2025-03-09T08:32:57Z)
• Adversarial Federated Consensus Learning for Surface Defect Classification Under Data Heterogeneity in IIoT [8.48069043458347]
  It's difficult to collect and centralize sufficient training data from various entities in Industrial Internet of Things (IIoT) Federated learning (FL) provides a solution by enabling collaborative global model training across clients. We propose a novel personalized FL approach, named Adversarial Federated Consensus Learning (AFedCL)
  arXiv  Detail & Related papers  (2024-09-24T03:59:32Z)
• SFedCA: Credit Assignment-Based Active Client Selection Strategy for Spiking Federated Learning [15.256986486372407]
  Spiking federated learning allows resource-constrained devices to train collaboratively at low power consumption without exchanging local data. Existing spiking federated learning methods employ a random selection approach for client aggregation, assuming unbiased client participation. We propose a credit assignment-based active client selection strategy, the SFedCA, to judiciously aggregate clients that contribute to the global sample distribution balance.
  arXiv  Detail & Related papers  (2024-06-18T01:56:22Z)
• An Aggregation-Free Federated Learning for Tackling Data Heterogeneity [50.44021981013037]
  Federated Learning (FL) relies on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framework, where clients update local models based on a global model aggregated by the server from the previous training round. We introduce FedAF, a novel aggregation-free FL algorithm.
  arXiv  Detail & Related papers  (2024-04-29T05:55:23Z)
• Personalized Federated Learning with Attention-based Client Selection [57.71009302168411]
  We propose FedACS, a new PFL algorithm with an Attention-based Client Selection mechanism. FedACS integrates an attention mechanism to enhance collaboration among clients with similar data distributions. Experiments on CIFAR10 and FMNIST validate FedACS's superiority.
  arXiv  Detail & Related papers  (2023-12-23T03:31:46Z)
• DCFL: Non-IID awareness Data Condensation aided Federated Learning [0.8158530638728501]
  Federated learning is a decentralized learning paradigm wherein a central server trains a global model iteratively by utilizing clients who possess a certain amount of private datasets. The challenge lies in the fact that the client side private data may not be identically and independently distributed. We propose DCFL which divides clients into groups by using the Centered Kernel Alignment (CKA) method, then uses dataset condensation methods with non-IID awareness to complete clients.
  arXiv  Detail & Related papers  (2023-12-21T13:04:24Z)
• FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data [54.81695390763957]
  Federated learning is an emerging distributed machine learning method. We propose a heterogeneous local variant of AMSGrad, named FedLALR, in which each client adjusts its learning rate. We show that our client-specified auto-tuned learning rate scheduling can converge and achieve linear speedup with respect to the number of clients.
  arXiv  Detail & Related papers  (2023-09-18T12:35:05Z)
• Federated Learning Model Aggregation in Heterogenous Aerial and Space Networks [36.283273000969636]
  Federated learning is a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs) We propose a novel framework that emphasizes updates from high-diversity clients and diminishes the influence of those from low-diversity clients. We show that WeiAvgCS could converge 46% faster on FashionMNIST and 38% faster on CIFAR10 than its benchmarks on average.
  arXiv  Detail & Related papers  (2023-05-24T21:34:36Z)
• Straggler-Resilient Personalized Federated Learning [55.54344312542944]
  Federated learning allows training models from samples distributed across a large network of clients while respecting privacy and communication restrictions. We develop a novel algorithmic procedure with theoretical speedup guarantees that simultaneously handles two of these hurdles. Our method relies on ideas from representation learning theory to find a global common representation using all clients' data and learn a user-specific set of parameters leading to a personalized solution for each client.
  arXiv  Detail & Related papers  (2022-06-05T01:14:46Z)
• Towards Fair Federated Learning with Zero-Shot Data Augmentation [123.37082242750866]
  Federated learning has emerged as an important distributed learning paradigm, where a server aggregates a global model from many client-trained models while having no access to the client data. We propose a novel federated learning system that employs zero-shot data augmentation on under-represented data to mitigate statistical heterogeneity and encourage more uniform accuracy performance across clients in federated networks. We study two variants of this scheme, Fed-ZDAC (federated learning with zero-shot data augmentation at the clients) and Fed-ZDAS (federated learning with zero-shot data augmentation at the server).
  arXiv  Detail & Related papers  (2021-04-27T18:23:54Z)

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.