FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis

• URL: http://arxiv.org/abs/2412.18557v1
• Date: Tue, 24 Dec 2024 17:20:43 GMT
• Title: FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
• Authors: Guochen Yan, Luyuan Xie, Xinyi Gao, Wentao Zhang, Qingni Shen, Yuejian Fang, Zhonghai Wu,
• Abstract summary: We propose a novel federated learning method: textbfFederated learning via textbfValuable textbfCondensed textbfKnowledge (FedVCK) We enhance the quality of condensed knowledge and select the most necessary knowledge guided by models, to tackle the non-IID problem within limited communication budgets effectively.
• Score: 27.843757290938925
• License:
• Abstract: Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distribution is always non-independent and identical distribution (non-IID), resulting in client drift and unsatisfactory performance. Despite existing federated learning methods attempting to solve the non-IID problems, they still show marginal advantages but rely on frequent communication which would incur high costs and privacy concerns. In this paper, we propose a novel federated learning method: \textbf{Fed}erated learning via \textbf{V}aluable \textbf{C}ondensed \textbf{K}nowledge (FedVCK). We enhance the quality of condensed knowledge and select the most necessary knowledge guided by models, to tackle the non-IID problem within limited communication budgets effectively. Specifically, on the client side, we condense the knowledge of each client into a small dataset and further enhance the condensation procedure with latent distribution constraints, facilitating the effective capture of high-quality knowledge. During each round, we specifically target and condense knowledge that has not been assimilated by the current model, thereby preventing unnecessary repetition of homogeneous knowledge and minimizing the frequency of communications required. On the server side, we propose relational supervised contrastive learning to provide more supervision signals to aid the global model updating. Comprehensive experiments across various medical tasks show that FedVCK can outperform state-of-the-art methods, demonstrating that it's non-IID robust and communication-efficient.

Related papers

• KnFu: Effective Knowledge Fusion [5.305607095162403]
  Federated Learning (FL) has emerged as a prominent alternative to the traditional centralized learning approach. The paper proposes Effective Knowledge Fusion (KnFu) algorithm that evaluates knowledge of local models to only fuse semantic neighbors' effective knowledge for each client. A key conclusion of the work is that in scenarios with large and highly heterogeneous local datasets, local training could be preferable to knowledge fusion-based solutions.
  arXiv  Detail & Related papers  (2024-03-18T15:49:48Z)
• UNIDEAL: Curriculum Knowledge Distillation Federated Learning [17.817181326740698]
  Federated Learning (FL) has emerged as a promising approach to enable collaborative learning among multiple clients. In this paper, we present UNI, a novel FL algorithm specifically designed to tackle the challenges of cross-domain scenarios. Our results demonstrate that UNI achieves superior performance in terms of both model accuracy and communication efficiency.
  arXiv  Detail & Related papers  (2023-09-16T11:30:29Z)
• Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good Teacher [52.2926020848095]
  Federated learning is vulnerable to white-box attacks and struggles to adapt to heterogeneous clients. This paper proposes a selective knowledge sharing mechanism for FD, termed Selective-FD.
  arXiv  Detail & Related papers  (2023-04-04T12:04:19Z)
• Combating Exacerbated Heterogeneity for Robust Models in Federated Learning [91.88122934924435]
  Combination of adversarial training and federated learning can lead to the undesired robustness deterioration. We propose a novel framework called Slack Federated Adversarial Training (SFAT) We verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets.
  arXiv  Detail & Related papers  (2023-03-01T06:16:15Z)
• When Do Curricula Work in Federated Learning? [56.88941905240137]
  We find that curriculum learning largely alleviates non-IIDness. The more disparate the data distributions across clients the more they benefit from learning. We propose a novel client selection technique that benefits from the real-world disparity in the clients.
  arXiv  Detail & Related papers  (2022-12-24T11:02:35Z)
• Uncertainty Minimization for Personalized Federated Semi-Supervised Learning [15.123493340717303]
  We propose a novel semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents) Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partial labeled data.
  arXiv  Detail & Related papers  (2022-05-05T04:41:27Z)
• FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation [54.2658887073461]
  Dealing with non-IID data is one of the most challenging problems for federated learning. This paper studies the joint problem of non-IID and long-tailed data in federated learning and proposes a corresponding solution called Federated Ensemble Distillation with Imbalance (FEDIC) FEDIC uses model ensemble to take advantage of the diversity of models trained on non-IID data.
  arXiv  Detail & Related papers  (2022-04-30T06:17:36Z)
• Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching [58.26619456972598]
  Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. This paper studies a practical yet challenging FL problem, named textitFederated Semi-supervised Learning (FSSL) We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme.
  arXiv  Detail & Related papers  (2021-06-16T07:58:00Z)
• Federated Continual Learning with Weighted Inter-client Transfer [79.93004004545736]
  We propose a novel federated continual learning framework, Federated Weighted Inter-client Transfer (FedWeIT) FedWeIT decomposes the network weights into global federated parameters and sparse task-specific parameters, and each client receives selective knowledge from other clients. We validate our FedWeIT against existing federated learning and continual learning methods, and our model significantly outperforms them with a large reduction in the communication cost.
  arXiv  Detail & Related papers  (2020-03-06T13:33:48Z)

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.