FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation

• URL: http://arxiv.org/abs/2503.18981v1
• Date: Sun, 23 Mar 2025 05:33:10 GMT
• Title: FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation
• Authors: Ziqiao Weng, Weidong Cai, Bo Zhou,
• Abstract summary: Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing.<n>Model-heterogeneous FL (MHFL) allows clients to train personalized models with heterogeneous architectures tailored to their computational resources and application-specific needs.<n>While peer-to-peer (P2P) FL removes server dependence, it suffers from model drift and knowledge dilution, limiting its effectiveness in heterogeneous settings.<n>We propose FedSKD, a novel MHFL framework that facilitates direct knowledge exchange through round-robin model circulation.
• Score: 7.944298319589845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated learning (FL) enables privacy-preserving collaborative model training without direct data sharing. Model-heterogeneous FL (MHFL) extends this paradigm by allowing clients to train personalized models with heterogeneous architectures tailored to their computational resources and application-specific needs. However, existing MHFL methods predominantly rely on centralized aggregation, which introduces scalability and efficiency bottlenecks, or impose restrictions requiring partially identical model architectures across clients. While peer-to-peer (P2P) FL removes server dependence, it suffers from model drift and knowledge dilution, limiting its effectiveness in heterogeneous settings. To address these challenges, we propose FedSKD, a novel MHFL framework that facilitates direct knowledge exchange through round-robin model circulation, eliminating the need for centralized aggregation while allowing fully heterogeneous model architectures across clients. FedSKD's key innovation lies in multi-dimensional similarity knowledge distillation, which enables bidirectional cross-client knowledge transfer at batch, pixel/voxel, and region levels for heterogeneous models in FL. This approach mitigates catastrophic forgetting and model drift through progressive reinforcement and distribution alignment while preserving model heterogeneity. Extensive evaluations on fMRI-based autism spectrum disorder diagnosis and skin lesion classification demonstrate that FedSKD outperforms state-of-the-art heterogeneous and homogeneous FL baselines, achieving superior personalization (client-specific accuracy) and generalization (cross-institutional adaptability). These findings underscore FedSKD's potential as a scalable and robust solution for real-world medical federated learning applications.

Related papers

• Client Contribution Normalization for Enhanced Federated Learning [4.726250115737579]
  Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data. Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across decentralized devices without data sharing. This paper focuses on data-dependent heterogeneity in FL and proposes a novel approach leveraging mean latent representations extracted from locally trained models.
  arXiv  Detail & Related papers  (2024-11-10T04:03:09Z)
• FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning [9.084674176224109]
  Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. We introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information.
  arXiv  Detail & Related papers  (2024-10-17T22:47:19Z)
• FedTSA: A Cluster-based Two-Stage Aggregation Method for Model-heterogeneous Federated Learning [10.254813698006103]
  FedTSA is a cluster-based two-stage aggregation method tailored for system heterogeneity in federated learning (FL) We show that FedTSA outperforms the baselines and explores various factors influencing model performance.
  arXiv  Detail & Related papers  (2024-07-06T14:59:55Z)
• 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)
• FedImpro: Measuring and Improving Client Update in Federated Learning [77.68805026788836]
  Federated Learning (FL) models often experience client drift caused by heterogeneous data. We present an alternative perspective on client drift and aim to mitigate it by generating improved local models.
  arXiv  Detail & Related papers  (2024-02-10T18:14:57Z)
• Fake It Till Make It: Federated Learning with Consensus-Oriented Generation [52.82176415223988]
  We propose federated learning with consensus-oriented generation (FedCOG) FedCOG consists of two key components at the client side: complementary data generation and knowledge-distillation-based model training. Experiments on classical and real-world FL datasets show that FedCOG consistently outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-12-10T18:49:59Z)
• Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction [22.567754688492414]
  Cross-device Federated Learning (FL) faces significant challenges where low-end clients that could potentially make unique contributions are excluded from training large models due to their resource bottlenecks. Recent research efforts have focused on model-heterogeneous FL, by extracting reduced-size models from the global model and applying them to local clients accordingly. This paper presents a unifying framework for heterogeneous FL algorithms with online model extraction and provides a general convergence analysis for the first time.
  arXiv  Detail & Related papers  (2023-10-12T19:07:58Z)
• Deep Equilibrium Models Meet Federated Learning [71.57324258813675]
  This study explores the problem of Federated Learning (FL) by utilizing the Deep Equilibrium (DEQ) models instead of conventional deep learning networks. We claim that incorporating DEQ models into the federated learning framework naturally addresses several open problems in FL. To the best of our knowledge, this study is the first to establish a connection between DEQ models and federated learning.
  arXiv  Detail & Related papers  (2023-05-29T22:51:40Z)
• Is Aggregation the Only Choice? Federated Learning via Layer-wise Model Recombination [33.12164201146458]
  We propose a novel and FL paradigm named FedMR (Federated Model Recombination) The goal of FedMR is to guide the recombined models to be trained towards a flat area. Compared with state-of-the-art FL methods, FedMR can significantly improve the inference accuracy without exposing privacy of each client.
  arXiv  Detail & Related papers  (2023-05-18T05:58:24Z)
• Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation [1.121358474059223]
  Federated learning (FL) enables multiple sites to collaboratively train powerful deep models without compromising data privacy and security. Weakly supervised segmentation, which uses sparsely-grained supervision, is increasingly being paid attention to due to its great potential of reducing annotation costs. We propose a novel personalized FL framework for medical image segmentation, named FedICRA, which uniformly leverages heterogeneous weak supervision.
  arXiv  Detail & Related papers  (2023-04-12T06:32:08Z)
• 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)
• A Twin Neural Model for Uplift [59.38563723706796]
  Uplift is a particular case of conditional treatment effect modeling. We propose a new loss function defined by leveraging a connection with the Bayesian interpretation of the relative risk. We show our proposed method is competitive with the state-of-the-art in simulation setting and on real data from large scale randomized experiments.
  arXiv  Detail & Related papers  (2021-05-11T16:02:39Z)

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.