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

Test-time adaptation (TT A) 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 TT A 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. 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 TT A methods. Federated Learning (FL) enables decentralized training across a network of clients, such as smart-phones, hospitals, or IoT devices, without sharing raw data. This is critical in privacy-sensitive domains like mobile computing, healthcare, and smart environments McMahan et al. (2017); Chen et al. (2025); Noble et al. (2022); Liu et al. (2024). However, data in FL is often non-identically distributed (non-IID), evolves over time, and suffers from issues such as client drift, system heterogeneity, and catastrophic forgetting, which significantly hinder model convergence and generalization Kairouz et al. (2021); Zhao et al. (2018).