FedALA: Adaptive Local Aggregation for Personalized Federated Learning

• URL: http://arxiv.org/abs/2212.01197v4
• Date: Sun, 17 Sep 2023 09:10:44 GMT
• Title: FedALA: Adaptive Local Aggregation for Personalized Federated Learning
• Authors: Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan
• Abstract summary: A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. We propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains.
• Score: 33.000160383079496
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client. To address this, we propose a method Federated learning with Adaptive Local Aggregation (FedALA) by capturing the desired information in the global model for client models in personalized FL. The key component of FedALA is an Adaptive Local Aggregation (ALA) module, which can adaptively aggregate the downloaded global model and local model towards the local objective on each client to initialize the local model before training in each iteration. To evaluate the effectiveness of FedALA, we conduct extensive experiments with five benchmark datasets in computer vision and natural language processing domains. FedALA outperforms eleven state-of-the-art baselines by up to 3.27% in test accuracy. Furthermore, we also apply ALA module to other federated learning methods and achieve up to 24.19% improvement in test accuracy.

Related papers

• 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)
• FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning [27.28589196972422]
  Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. FL often suffers from significant performance degradation when clients have heterogeneous data distributions. We propose a novel method, Federated Stabilized Orthogonal Learning (FedSOL), which balances local and global learning.
  arXiv  Detail & Related papers  (2023-08-24T03:43:02Z)
• 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)
• Integrating Local Real Data with Global Gradient Prototypes for Classifier Re-Balancing in Federated Long-Tailed Learning [60.41501515192088]
  Federated Learning (FL) has become a popular distributed learning paradigm that involves multiple clients training a global model collaboratively. The data samples usually follow a long-tailed distribution in the real world, and FL on the decentralized and long-tailed data yields a poorly-behaved global model. In this work, we integrate the local real data with the global gradient prototypes to form the local balanced datasets.
  arXiv  Detail & Related papers  (2023-01-25T03:18:10Z)
• Personalized Federated Learning with Hidden Information on Personalized Prior [18.8426865970643]
  We propose pFedBreD, a framework to solve the problem we model using Bregman divergence regularization. Our experiments show that our proposal significantly outcompetes other PFL algorithms on multiple public benchmarks.
  arXiv  Detail & Related papers  (2022-11-19T12:45:19Z)
• Self-Aware Personalized Federated Learning [32.97492968378679]
  We develop a self-aware personalized federated learning (FL) method inspired by Bayesian hierarchical models. Our method uses uncertainty-driven local training steps and aggregation rule instead of conventional local fine-tuning and sample size-based aggregation. With experimental studies on synthetic data, Amazon Alexa audio data, and public datasets such as MNIST, FEMNIST, CIFAR10, and Sent140, we show that our proposed method can achieve significantly improved personalization performance.
  arXiv  Detail & Related papers  (2022-04-17T19:02:25Z)
• 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)
• Fine-tuning Global Model via Data-Free Knowledge Distillation for Non-IID Federated Learning [86.59588262014456]
  Federated Learning (FL) is an emerging distributed learning paradigm under privacy constraint. We propose a data-free knowledge distillation method to fine-tune the global model in the server (FedFTG) Our FedFTG significantly outperforms the state-of-the-art (SOTA) FL algorithms and can serve as a strong plugin for enhancing FedAvg, FedProx, FedDyn, and SCAFFOLD.
  arXiv  Detail & Related papers  (2022-03-17T11:18:17Z)
• Federated and Generalized Person Re-identification through Domain and Feature Hallucinating [88.77196261300699]
  We study the problem of federated domain generalization (FedDG) for person re-identification (re-ID) We propose a novel method, called "Domain and Feature Hallucinating (DFH)", to produce diverse features for learning generalized local and global models. Our method achieves the state-of-the-art performance for FedDG on four large-scale re-ID benchmarks.
  arXiv  Detail & Related papers  (2022-03-05T09:15:13Z)
• 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)
• 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.