Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data

• URL: http://arxiv.org/abs/2504.14694v1
• Date: Sun, 20 Apr 2025 18:06:55 GMT
• Title: Learning Critically: Selective Self Distillation in Federated Learning on Non-IID Data
• Authors: Yuting He, Yiqiang Chen, XiaoDong Yang, Hanchao Yu, Yi-Hua Huang, Yang Gu,
• Abstract summary: We propose a Selective Self-Distillation method for Federated learning (FedSSD)<n>FedSSD imposes adaptive constraints on the local updates by self-distilling the global model's knowledge.<n>It achieves better generalization and robustness in fewer communication rounds, compared with other state-of-the-art FL methods.
• Score: 17.624808621195978
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning (FL) enables multiple clients to collaboratively train a global model while keeping local data decentralized. Data heterogeneity (non-IID) across clients has imposed significant challenges to FL, which makes local models re-optimize towards their own local optima and forget the global knowledge, resulting in performance degradation and convergence slowdown. Many existing works have attempted to address the non-IID issue by adding an extra global-model-based regularizing item to the local training but without an adaption scheme, which is not efficient enough to achieve high performance with deep learning models. In this paper, we propose a Selective Self-Distillation method for Federated learning (FedSSD), which imposes adaptive constraints on the local updates by self-distilling the global model's knowledge and selectively weighting it by evaluating the credibility at both the class and sample level. The convergence guarantee of FedSSD is theoretically analyzed and extensive experiments are conducted on three public benchmark datasets, which demonstrates that FedSSD achieves better generalization and robustness in fewer communication rounds, compared with other state-of-the-art FL methods.

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)
• FedMAP: Unlocking Potential in Personalized Federated Learning through Bi-Level MAP Optimization [11.040916982022978]
  Federated Learning (FL) enables collaborative training of machine learning models on decentralized data. Data across clients often differs significantly due to class imbalance, feature distribution skew, sample size imbalance, and other phenomena. We propose a novel Bayesian PFL framework using bi-level optimization to tackle the data heterogeneity challenges.
  arXiv  Detail & Related papers  (2024-05-29T11:28:06Z)
• Multi-level Personalized Federated Learning on Heterogeneous and Long-Tailed Data [10.64629029156029]
  We introduce an innovative personalized Federated Learning framework, Multi-level Personalized Federated Learning (MuPFL) MuPFL integrates three pivotal modules: Biased Activation Value Dropout (BAVD), Adaptive Cluster-based Model Update (ACMU) and Prior Knowledge-assisted Fine-tuning (PKCF) Experiments on diverse real-world datasets show that MuPFL consistently outperforms state-of-the-art baselines, even under extreme non-i.i.d. and long-tail conditions.
  arXiv  Detail & Related papers  (2024-05-10T11:52:53Z)
• Federated Learning with Projected Trajectory Regularization [65.6266768678291]
  Federated learning enables joint training of machine learning models from distributed clients without sharing their local data. One key challenge in federated learning is to handle non-identically distributed data across the clients. We propose a novel federated learning framework with projected trajectory regularization (FedPTR) for tackling the data issue.
  arXiv  Detail & Related papers  (2023-12-22T02:12:08Z)
• 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)
• 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)
• The Best of Both Worlds: Accurate Global and Personalized Models through Federated Learning with Data-Free Hyper-Knowledge Distillation [17.570719572024608]
  FedHKD (Federated Hyper-Knowledge Distillation) is a novel FL algorithm in which clients rely on knowledge distillation to train local models. Unlike other KD-based pFL methods, FedHKD does not rely on a public dataset nor it deploys a generative model at the server. We conduct extensive experiments on visual datasets in a variety of scenarios, demonstrating that FedHKD provides significant improvement in both personalized as well as global model performance.
  arXiv  Detail & Related papers  (2023-01-21T16:20:57Z)
• 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)
• 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.