pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data

• URL: http://arxiv.org/abs/2511.18066v1
• Date: Sat, 22 Nov 2025 14:01:41 GMT
• Title: pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data
• Authors: Md Akil Raihan Iftee, Syed Md. Ahnaf Hasan, Mir Sazzat Hossain, Rakibul Hasan Rajib, Amin Ahsan Ali, AKM Mahbubur Rahman, Sajib Mistry, Monowar Bhuyan,
• Abstract summary: pFedBBN is a personalized federated test-time adaptation framework.<n>It supports fully unsupervised local adaptation and introduces a class-aware model aggregation strategy.<n>It addresses both distribution shifts and class imbalance through balanced feature normalization and domain-aware collaboration.
• Score: 0.9087690587593468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time adaptation (TTA) in federated learning (FL) is crucial for handling unseen data distributions across clients, particularly when faced with domain shifts and skewed class distributions. Class Imbalance (CI) remains a fundamental challenge in FL, where rare but critical classes are often severely underrepresented in individual client datasets. Although prior work has addressed CI during training through reliable aggregation and local class distribution alignment, these methods typically rely on access to labeled data or coordination among clients, and none address class unsupervised adaptation to dynamic domains or distribution shifts at inference time under federated CI constraints. Revealing the failure of state-of-the-art TTA in federated client adaptation in CI scenario, we propose pFedBBN,a personalized federated test-time adaptation framework that employs balanced batch normalization (BBN) during local client adaptation to mitigate prediction bias by treating all classes equally, while also enabling client collaboration guided by BBN similarity, ensuring that clients with similar balanced representations reinforce each other and that adaptation remains aligned with domain-specific characteristics. pFedBBN supports fully unsupervised local adaptation and introduces a class-aware model aggregation strategy that enables personalized inference without compromising privacy. It addresses both distribution shifts and class imbalance through balanced feature normalization and domain-aware collaboration, without requiring any labeled or raw data from clients. Extensive experiments across diverse baselines show that pFedBBN consistently enhances robustness and minority-class performance over state-of-the-art FL and TTA methods.

Related papers

• Federated Learning with Profile Mapping under Distribution Shifts and Drifts [13.33580160559305]
  Federated Learning (FL) enables decentralized model training across clients without sharing raw data.<n>Existing methods often fail to address distribution shift across clients and distribution drift over time.<n>We introduce Feroma, a novel FL framework that explicitly handles both distribution shift and drift without relying on client or cluster identity.
  arXiv  Detail & Related papers  (2026-02-07T19:31:50Z)
• Federated Self-Supervised Learning for Automatic Modulation Classification under Non-IID and Class-Imbalanced Data [18.951106077022672]
  Federated learning avoids central aggregation by training on distributed clients.<n>We propose FedSSL-AMC, which trains a causal, time-dilated CNN with triplet-loss self-supervision on unlabeled I/Q sequences across clients, followed by per-client SVMs on small labeled sets.
  arXiv  Detail & Related papers  (2025-10-06T15:37:15Z)
• Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning [1.3270838622986498]
  Federated learning (FL) enables collaborative model training across distributed clients while preserving data privacy by keeping data local.<n>Traditional FL approaches rely on a centralized, star-shaped topology, where a central server aggregates model updates from clients.<n>We propose a decentralized, peer-to-peer (P2P) FL framework to enable each client to identify and aggregate a personalized set of trustworthy and beneficial updates.
  arXiv  Detail & Related papers  (2025-08-07T10:10:37Z)
• Interaction-Aware Gaussian Weighting for Clustered Federated Learning [58.92159838586751]
  Federated Learning (FL) emerged as a decentralized paradigm to train models while preserving privacy.<n>We propose a novel clustered FL method, FedGWC (Federated Gaussian Weighting Clustering), which groups clients based on their data distribution.<n>Our experiments on benchmark datasets show that FedGWC outperforms existing FL algorithms in cluster quality and classification accuracy.
  arXiv  Detail & Related papers  (2025-02-05T16:33:36Z)
• TRAIL: Trust-Aware Client Scheduling for Semi-Decentralized Federated Learning [13.144501509175985]
  We propose a TRust-Aware clIent scheduLing mechanism called TRAIL, which assesses client states and contributions.<n>We focus on a semi-decentralized FL framework where edge servers and clients train a shared global model using unreliable intra-cluster model aggregation and inter-cluster model consensus.<n>Experiments conducted on real-world datasets demonstrate that TRAIL outperforms state-of-the-art baselines, achieving an improvement of 8.7% in test accuracy and a reduction of 15.3% in training loss.
  arXiv  Detail & Related papers  (2024-12-16T05:02:50Z)
• An Architecture Built for Federated Learning: Addressing Data Heterogeneity through Adaptive Normalization-Free Feature Recalibration [0.3481075494213406]
  We propose Adaptive Normalization-free Feature Recalibration (ANFR) to combat heterogeneous data in Federated Learning (FL)<n>ANFR combines weight standardization and channel attention to produce learnable scaling factors for feature maps.<n>Experiments show ANFR consistently outperforms established baselines across various aggregation methods.
  arXiv  Detail & Related papers  (2024-10-02T20:16:56Z)
• Class-Wise Federated Averaging for Efficient Personalization [0.8287206589886879]
  Federated learning (FL) enables collaborative model training across distributed clients without centralizing data.<n>We propose Class-wise Federated Averaging (cwFedAvg), a novel personalized FL (PFL) framework that performs Federated Averaging for each class.<n>We also propose Weight Distribution Regularizer (WDR), which encourages deep networks to encode class-specific information efficiently.
  arXiv  Detail & Related papers  (2024-06-12T01:32:24Z)
• Towards Instance-adaptive Inference for Federated Learning [80.38701896056828]
  Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. In this paper, we present a novel FL algorithm, i.e., FedIns, to handle intra-client data heterogeneity by enabling instance-adaptive inference in the FL framework. Our experiments show that our FedIns outperforms state-of-the-art FL algorithms, e.g., a 6.64% improvement against the top-performing method with less than 15% communication cost on Tiny-ImageNet.
  arXiv  Detail & Related papers  (2023-08-11T09:58:47Z)
• FedABC: Targeting Fair Competition in Personalized Federated Learning [76.9646903596757]
  Federated learning aims to collaboratively train models without accessing their client's local private data. We propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the one-vs-all'' training strategy in each client to alleviate the unfair competition between classes.
  arXiv  Detail & Related papers  (2023-02-15T03:42:59Z)
• 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)
• 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)
• 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)
• Robust Convergence in Federated Learning through Label-wise Clustering [6.693651193181458]
  Non-IID dataset and heterogeneous environment of the local clients are regarded as a major issue in Federated Learning (FL) We propose a novel Label-wise clustering algorithm that guarantees the trainability among geographically heterogeneous local clients. Our paper shows that proposed Label-wise clustering demonstrates prompt and robust convergence compared to other FL algorithms.
  arXiv  Detail & Related papers  (2021-12-28T18:13:09Z)

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.