FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning

• URL: http://arxiv.org/abs/2608.23031v2
• Date: Wed, 26 Aug 2026 17:35:47 GMT
• Title: FedCC: Towards Addressing Label Distribution Skews in Distillation-Based Federated Learning
• Abstract summary: Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data.<n>In distillation-based FL, each client applies its local model on an unlabeled public dataset, and shares only prediction results with the server.<n>We propose FedCC, a simple and effective algorithm for mitigating client misclassification.
• Score: 6.693752118202639
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Federated Learning (FL) enables distributed clients to collaboratively train models without sharing raw data, making it promising for leveraging massive devices in communication networks. In distillation-based FL, each client applies its local model on an unlabeled public dataset, and shares only prediction results with the server. While heterogeneous local data introduces label distribution skew, thus biasing client models toward majority classes and leading to potentially inaccurate predictions. The lack of ground-truth labels in the public dataset hampers the server's ability to calibrate predictions, which ultimately degrades overall performance. To address this, we propose FedCC, a simple and effective algorithm for mitigating client misclassification. Instead of being forced to classify and risking error propagation, clients are allowed to tag ambiguous samples as 'unknown'. This additional class, together with calibrated pseudo-labels on the public data, balances confidence in majority classes against uncertainty in under-represented ones. Extensive experiments demonstrate that FedCC significantly outperforms existing methods, especially under severe label skew. In the extreme scenario where each client holds samples from only one of ten classes, FedCC achieves 67.3% accuracy, while baselines collapse to near-random results.

Related papers

• Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead [11.115044922684545]
  Federated ensemble distillation addresses client heterogeneity by generating pseudo-labels for an unlabeled server dataset based on client predictions.<n>We propose a provably near-optimal weighting method that leverages client discriminators trained with a server-distributed generator and local datasets.<n>Our experiments on various image classification tasks demonstrate that the proposed method significantly outperforms baselines.
  arXiv  Detail & Related papers  (2025-02-10T10:58:57Z)
• Uncertainty-Aware Label Refinement on Hypergraphs for Personalized Federated Facial Expression Recognition [58.98052764581606]
  We develop a novel uncertainty-Aware label refineMent on hYpergraphs (AMY) method.<n>For local training, each local model consists of a backbone, an uncertainty estimation (UE) block, and an expression classification (EC) block.<n>A personalized uncertainty estimator is then introduced to estimate reliable uncertainty weights of samples in the local client.
  arXiv  Detail & Related papers  (2025-01-03T13:59:21Z)
• Boosting Federated Learning with FedEntOpt: Mitigating Label Skew by Entropy-Based Client Selection [13.851391819710367]
  Deep learning domains typically require an extensive amount of data for optimal performance.<n>FedEntOpt is designed to mitigate performance issues caused by label distribution skew.<n>It exhibits robust and superior performance in scenarios with low participation rates and client dropout.
  arXiv  Detail & Related papers  (2024-11-02T13:31:36Z)
• (FL)$^2$: Overcoming Few Labels in Federated Semi-Supervised Learning [4.803231218533992]
  Federated Learning (FL) is a distributed machine learning framework that trains accurate global models while preserving clients' privacy-sensitive data. Most FL approaches assume that clients possess labeled data, which is often not the case in practice. We propose $(FL)2$, a robust training method for unlabeled clients using sharpness-aware consistency regularization.
  arXiv  Detail & Related papers  (2024-10-30T17:15:02Z)
• Federated Learning with Label-Masking Distillation [33.80340338038264]
  Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients. Due to the different user behavior of the client, label distributions between different clients are significantly different. We propose a label-masking distillation approach termed FedLMD to facilitate federated learning via perceiving the various label distributions of each client.
  arXiv  Detail & Related papers  (2024-09-20T00:46:04Z)
• Federated Learning with Only Positive Labels by Exploring Label Correlations [78.59613150221597]
  Federated learning aims to collaboratively learn a model by using the data from multiple users under privacy constraints. In this paper, we study the multi-label classification problem under the federated learning setting. We propose a novel and generic method termed Federated Averaging by exploring Label Correlations (FedALC)
  arXiv  Detail & Related papers  (2024-04-24T02:22:50Z)
• Exploring Vacant Classes in Label-Skewed Federated Learning [113.65301899666645]
  This paper introduces FedVLS, a novel approach to label-skewed federated learning.<n>It integrates vacant-class distillation and logit suppression simultaneously.<n>Experiments validate the efficacy of FedVLS, demonstrating superior performance compared to previous state-of-the-art (SOTA) methods.
  arXiv  Detail & Related papers  (2024-01-04T16:06:31Z)
• FedIL: Federated Incremental Learning from Decentralized Unlabeled Data with Convergence Analysis [23.70951896315126]
  This work considers the server with a small labeled dataset and intends to use unlabeled data in multiple clients for semi-supervised learning. We propose a new framework with a generalized model, Federated Incremental Learning (FedIL), to address the problem of how to utilize labeled data in the server and unlabeled data in clients separately.
  arXiv  Detail & Related papers  (2023-02-23T07:12:12Z)
• Fed-CBS: A Heterogeneity-Aware Client Sampling Mechanism for Federated Learning via Class-Imbalance Reduction [76.26710990597498]
  We show that the class-imbalance of the grouped data from randomly selected clients can lead to significant performance degradation. Based on our key observation, we design an efficient client sampling mechanism, i.e., Federated Class-balanced Sampling (Fed-CBS) In particular, we propose a measure of class-imbalance and then employ homomorphic encryption to derive this measure in a privacy-preserving way.
  arXiv  Detail & Related papers  (2022-09-30T05:42:56Z)
• Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients [98.22390453672499]
  Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients.
  arXiv  Detail & Related papers  (2022-04-07T09:12:00Z)
• 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.