Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning

• URL: http://arxiv.org/abs/2406.07800v1
• Date: Wed, 12 Jun 2024 01:32:24 GMT
• Title: Regularizing and Aggregating Clients with Class Distribution for Personalized Federated Learning
• Authors: Gyuejeong Lee, Daeyoung Choi,
• Abstract summary: Class-wise Federated Averaging (cwFedAVG) class-wise, creating multiple global models per class on the server. Each local model integrates these global models weighted by its estimated local class distribution, derived from the L2-norms of deep network weights. We also newly designed Weight Distribution Regularizer (WDR) to further enhance the accuracy of estimating a local class distribution.
• Score: 0.8287206589886879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized federated learning (PFL) enables customized models for clients with varying data distributions. However, existing PFL methods often incur high computational and communication costs, limiting their practical application. This paper proposes a novel PFL method, Class-wise Federated Averaging (cwFedAVG), that performs Federated Averaging (FedAVG) class-wise, creating multiple global models per class on the server. Each local model integrates these global models weighted by its estimated local class distribution, derived from the L2-norms of deep network weights, avoiding privacy violations. Afterward, each global model does the same with local models using the same method. We also newly designed Weight Distribution Regularizer (WDR) to further enhance the accuracy of estimating a local class distribution by minimizing the Euclidean distance between the class distribution and the weight norms' distribution. Experimental results demonstrate that cwFedAVG matches or outperforms several existing PFL methods. Notably, cwFedAVG is conceptually simple yet computationally efficient as it mitigates the need for extensive calculation to collaborate between clients by leveraging shared global models. Visualizations provide insights into how cwFedAVG enables local model specialization on respective class distributions while global models capture class-relevant information across clients.

Related papers

• Personalized Federated Learning via Feature Distribution Adaptation [3.410799378893257]
  Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model. personalized federated learning (PFL) seeks to address this by learning individual models tailored to each client. We propose an algorithm, pFedFDA, that efficiently generates personalized models by adapting global generative classifiers to their local feature distributions.
  arXiv  Detail & Related papers  (2024-11-01T03:03:52Z)
• Tunable Soft Prompts are Messengers in Federated Learning [55.924749085481544]
  Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources. The lack of model privacy protection in FL becomes an unneglectable challenge. We propose a novel FL training approach that accomplishes information exchange among participants via tunable soft prompts.
  arXiv  Detail & Related papers  (2023-11-12T11:01:10Z)
• 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)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation [32.36334319329364]
  Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers. Recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. We propose a novel federated model soup method to optimize the trade-off between local and global performance.
  arXiv  Detail & Related papers  (2023-07-20T00:07:29Z)
• Efficient Personalized Federated Learning via Sparse Model-Adaptation [47.088124462925684]
  Federated Learning (FL) aims to train machine learning models for multiple clients without sharing their own private data. We propose pFedGate for efficient personalized FL by adaptively and efficiently learning sparse local models. We show that pFedGate achieves superior global accuracy, individual accuracy and efficiency simultaneously over state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-04T12:21:34Z)
• Visual Prompt Based Personalized Federated Learning [83.04104655903846]
  We propose a novel PFL framework for image classification tasks, dubbed pFedPT, that leverages personalized visual prompts to implicitly represent local data distribution information of clients. Experiments on the CIFAR10 and CIFAR100 datasets show that pFedPT outperforms several state-of-the-art (SOTA) PFL algorithms by a large margin in various settings.
  arXiv  Detail & Related papers  (2023-03-15T15:02:15Z)
• GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization [6.592268037926868]
  We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Our results show that our GRP-FED improves under both global and local scenarios.
  arXiv  Detail & Related papers  (2021-08-31T14:09:04Z)
• A Bayesian Federated Learning Framework with Online Laplace Approximation [144.7345013348257]
  Federated learning allows multiple clients to collaboratively learn a globally shared model. We propose a novel FL framework that uses online Laplace approximation to approximate posteriors on both the client and server side. We achieve state-of-the-art results on several benchmarks, clearly demonstrating the advantages of the proposed method.
  arXiv  Detail & Related papers  (2021-02-03T08:36:58Z)
• Personalized Federated Learning with First Order Model Optimization [76.81546598985159]
  We propose an alternative to federated learning, where each client federates with other relevant clients to obtain a stronger model per client-specific objectives. We do not assume knowledge of underlying data distributions or client similarities, and allow each client to optimize for arbitrary target distributions of interest. Our method outperforms existing alternatives, while also enabling new features for personalized FL such as transfer outside of local data distributions.
  arXiv  Detail & Related papers  (2020-12-15T19:30:29Z)

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.