Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors

• URL: http://arxiv.org/abs/2405.11525v1
• Date: Sun, 19 May 2024 11:36:45 GMT
• Title: Overcoming Data and Model Heterogeneities in Decentralized Federated Learning via Synthetic Anchors
• Authors: Chun-Yin Huang, Kartik Srinivas, Xin Zhang, Xiaoxiao Li,
• Abstract summary: Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless network that allows clients to own and optimize different local models separately. We propose a novel Decentralized FL technique by introducing Synthetic Anchors, dubbed as DeSA.
• Score: 21.931436901703634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional Federated Learning (FL) involves collaborative training of a global model while maintaining user data privacy. One of its branches, decentralized FL, is a serverless network that allows clients to own and optimize different local models separately, which results in saving management and communication resources. Despite the promising advancements in decentralized FL, it may reduce model generalizability due to lacking a global model. In this scenario, managing data and model heterogeneity among clients becomes a crucial problem, which poses a unique challenge that must be overcome: How can every client's local model learn generalizable representation in a decentralized manner? To address this challenge, we propose a novel Decentralized FL technique by introducing Synthetic Anchors, dubbed as DeSA. Based on the theory of domain adaptation and Knowledge Distillation (KD), we theoretically and empirically show that synthesizing global anchors based on raw data distribution facilitates mutual knowledge transfer. We further design two effective regularization terms for local training: 1) REG loss that regularizes the distribution of the client's latent embedding with the anchors and 2) KD loss that enables clients to learn from others. Through extensive experiments on diverse client data distributions, we showcase the effectiveness of DeSA in enhancing both inter- and intra-domain accuracy of each client.

Related papers

• 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)
• 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)
• Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning [9.975023463908496]
  Federated Learning (FL) is a machine learning paradigm that enables clients to jointly train a global model by aggregating the locally trained models without sharing any local training data. We propose a novel regularization technique based on adaptive self-distillation (ASD) for training models on the client side. Our regularization scheme adaptively adjusts to the client's training data based on the global model entropy and the client's label distribution.
  arXiv  Detail & Related papers  (2023-05-31T07:00:42Z)
• 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)
• FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning [87.08902493524556]
  Federated learning(FL) has recently attracted increasing attention from academia and industry. We propose FedDM to build the global training objective from multiple local surrogate functions. In detail, we construct synthetic sets of data on each client to locally match the loss landscape from original data.
  arXiv  Detail & Related papers  (2022-07-20T04:55:18Z)
• 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)
• 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)
• FedH2L: Federated Learning with Model and Statistical Heterogeneity [75.61234545520611]
  Federated learning (FL) enables distributed participants to collectively learn a strong global model without sacrificing their individual data privacy. We introduce FedH2L, which is agnostic to both the model architecture and robust to different data distributions across participants. In contrast to approaches sharing parameters or gradients, FedH2L relies on mutual distillation, exchanging only posteriors on a shared seed set between participants in a decentralized manner.
  arXiv  Detail & Related papers  (2021-01-27T10:10:18Z)
• Decentralized Federated Learning via Mutual Knowledge Transfer [37.5341683644709]
  Decentralized federated learning (DFL) is a problem in the Internet of things (IoT) systems. We propose a mutual knowledge transfer (Def-KT) algorithm where local clients fuse models by transferring their learnt knowledge to each other. Our experiments on the MNIST, Fashion-MNIST, and CIFAR10 datasets reveal datasets that the proposed Def-KT algorithm significantly outperforms the baseline DFL methods.
  arXiv  Detail & Related papers  (2020-12-24T01:43:53Z)

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.