FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration

• URL: http://arxiv.org/abs/2505.18981v1
• Date: Sun, 25 May 2025 05:24:49 GMT
• Title: FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
• Authors: Huan Wang, Haoran Li, Huaming Chen, Jun Yan, Lijuan Wang, Jiahua Shi, Shiping Chen, Jun Shen,
• Abstract summary: Key idea of FedSKC is to extract and transfer domain preferences from interclient data distributions.<n>FedSKC comprises three components: contrastive learning, global discrepancy aggregation, and global period review.
• Score: 43.25824181502647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancement of edge computing, federated learning (FL) displays a bright promise as a privacy-preserving collaborative learning paradigm. However, one major challenge for FL is the data heterogeneity issue, which refers to the biased labeling preferences among multiple clients, negatively impacting convergence and model performance. Most previous FL methods attempt to tackle the data heterogeneity issue locally or globally, neglecting underlying class-wise structure information contained in each client. In this paper, we first study how data heterogeneity affects the divergence of the model and decompose it into local, global, and sampling drift sub-problems. To explore the potential of using intra-client class-wise structural knowledge in handling these drifts, we thus propose Federated Learning with Structural Knowledge Collaboration (FedSKC). The key idea of FedSKC is to extract and transfer domain preferences from inter-client data distributions, offering diverse class-relevant knowledge and a fair convergent signal. FedSKC comprises three components: i) local contrastive learning, to prevent weight divergence resulting from local training; ii) global discrepancy aggregation, which addresses the parameter deviation between the server and clients; iii) global period review, correcting for the sampling drift introduced by the server randomly selecting devices. We have theoretically analyzed FedSKC under non-convex objectives and empirically validated its superiority through extensive experimental results.

Related papers

• FedSC: Federated Learning with Semantic-Aware Collaboration [12.366529890744822]
  Federated learning (FL) aims to train models collaboratively across clients without sharing data for privacy-preserving.<n>We propose Federated Learning with Semantic-Aware Collaboration (FedSC) to capture client-specific and class-relevant knowledge across heterogeneous clients.
  arXiv  Detail & Related papers  (2025-06-26T05:04:55Z)
• 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)
• 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)
• Rethinking Client Drift in Federated Learning: A Logit Perspective [125.35844582366441]
  Federated Learning (FL) enables multiple clients to collaboratively learn in a distributed way, allowing for privacy protection. We find that the difference in logits between the local and global models increases as the model is continuously updated. We propose a new algorithm, named FedCSD, a Class prototype Similarity Distillation in a federated framework to align the local and global models.
  arXiv  Detail & Related papers  (2023-08-20T04:41:01Z)
• FedCME: Client Matching and Classifier Exchanging to Handle Data Heterogeneity in Federated Learning [5.21877373352943]
  Data heterogeneity across clients is one of the key challenges in Federated Learning (FL) We propose a novel FL framework named FedCME by client matching and classifier exchanging. Experimental results demonstrate that FedCME performs better than FedAvg, FedProx, MOON and FedRS on popular federated learning benchmarks.
  arXiv  Detail & Related papers  (2023-07-17T15:40:45Z)
• Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks [65.34113135080105]
  We show that not only the issue of data heterogeneity in current setups is not necessarily a problem but also in fact it can be beneficial for the FL participants. Our observations are intuitive. Our code is available at https://github.com/MMorafah/FL-SC-NIID.
  arXiv  Detail & Related papers  (2022-09-30T17:15:19Z)
• FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction [48.85303253333453]
  Federated learning (FL) allows multiple clients to collectively train a high-performance global model without sharing their private data. We propose a novel federated learning algorithm with local drift decoupling and correction (FedDC) Our FedDC only introduces lightweight modifications in the local training phase, in which each client utilizes an auxiliary local drift variable to track the gap between the local model parameter and the global model parameters. Experiment results and analysis demonstrate that FedDC yields expediting convergence and better performance on various image classification tasks.
  arXiv  Detail & Related papers  (2022-03-22T14:06:26Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)

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.