# Test-Time Robust Personalization for Federated Learning

arXiv: https://arxiv.org/abs/2205.10920 · HTML: https://mlsanity.com/paper/2205.10920/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- Builds on 90 of its 121 references that are in the map.
- First posted on arXiv 22 May 2022.

## Builds on (most cited first, 30 of 90)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [WILDS: A Benchmark of in-the-Wild Distribution Shifts](https://mlsanity.com/paper/2012.07421/) (2020)
- [Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization](https://mlsanity.com/paper/1911.08731/) (2019)
- [In Search of Lost Domain Generalization](https://mlsanity.com/paper/2007.01434/) (2020)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)

## Built on by (most cited first, 30 of 39)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark](https://mlsanity.com/paper/2311.06750/) (2023)
- [When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions](https://mlsanity.com/paper/2306.15546/) (2023)
- [On Pitfalls of Test-Time Adaptation](https://mlsanity.com/paper/2306.03536/) (2023)
- [Dual-Personalizing Adapter for Federated Foundation Models](https://mlsanity.com/paper/2403.19211/) (2024)
- [FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction](https://mlsanity.com/paper/2205.13462/) (2022)
- [Adaptive Test-Time Personalization for Federated Learning](https://mlsanity.com/paper/2310.18816/) (2023)
- [PuzzleAvatar: Assembling 3D Avatars from Personal Albums](https://mlsanity.com/paper/2405.14869/) (2024)
- [FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clustering](https://mlsanity.com/paper/2301.12379/) (2023)
- [PerAda: Parameter-Efficient Federated Learning Personalization with Generalization Guarantees](https://mlsanity.com/paper/2302.06637/) (2023)
- [FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detection](https://mlsanity.com/paper/2410.11397/) (2024)
- [FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models](https://mlsanity.com/paper/2501.02461/) (2025)
- [Straggler-Resilient Personalized Federated Learning](https://mlsanity.com/paper/2206.02078/) (2022)
- [Profit: Benchmarking Personalization and Robustness Trade-off in Federated Prompt Tuning](https://mlsanity.com/paper/2310.04627/) (2023)
- [ZooPFL: Exploring Black-box Foundation Models for Personalized Federated Learning](https://mlsanity.com/paper/2310.05143/) (2023)
- [Latte: Collaborative Test-Time Adaptation of Vision-Language Models in Federated Learning](https://mlsanity.com/paper/2507.21494/) (2025)
- [FediOS: Decoupling Orthogonal Subspaces for Personalization in Feature-skew Federated Learning](https://mlsanity.com/paper/2311.18559/) (2023)
- [Combating Exacerbated Heterogeneity for Robust Models in Federated Learning](https://mlsanity.com/paper/2303.00250/) (2023)
- [Federated Continual Novel Class Learning](https://mlsanity.com/paper/2312.13500/) (2023)
- [Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging](https://mlsanity.com/paper/2306.01176/) (2023)
- [UPFL: Unsupervised Personalized Federated Learning towards New Clients](https://mlsanity.com/paper/2307.15994/) (2023)
- [Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies](https://mlsanity.com/paper/2310.05397/) (2023)
- [An Empirical Evaluation of Federated Contextual Bandit Algorithms](https://mlsanity.com/paper/2303.10218/) (2023)
- [Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching](https://mlsanity.com/paper/2403.17423/) (2024)
- [ProFL: Performative Robust Optimal Federated Learning](https://mlsanity.com/paper/2410.18075/) (2024)
- [Federated Adapter on Foundation Models: An Out-Of-Distribution Approach](https://mlsanity.com/paper/2505.01075/) (2025)
- [FedCTTA: A Collaborative Approach to Continual Test-Time Adaptation in Federated Learning](https://mlsanity.com/paper/2505.13643/) (2025)
- [FOCoOp: Enhancing Out-of-Distribution Robustness in Federated Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2506.16218/) (2025)
- [Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clustering](https://mlsanity.com/paper/2407.19331/) (2024)
- [pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data](https://mlsanity.com/paper/2511.18066/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is a machine learning paradigm where many clients collaboratively learn a shared global model with decentralized training data. Personalized FL additionally adapts the global model to different clients, achieving promising results on consistent local training and test distributions. However, for real-world personalized FL applications, it is crucial to go one step further: robustifying FL models under the evolving local test set during deployment, where various distribution shifts can arise. In this work, we identify the pitfalls of existing works under test-time distribution shifts and propose Federated Test-time Head Ensemble plus tuning(FedTHE+), which personalizes FL models with robustness to various test-time distribution shifts. We illustrate the advancement of FedTHE+ (and its computationally efficient variant FedTHE) over strong competitors, by training various neural architectures (CNN, ResNet, and Transformer) on CIFAR10 andImageNet with various test distributions. Along with this, we build a benchmark for assessing the performance and robustness of personalized FL methods during deployment. Code: https://github.com/LINs-lab/FedTHE.