Title: FLUX: Efficient Descriptor-Driven Clustered Federated Learning under Arbitrary Distribution Shifts
Venue: NeurIPS 2025
Accepted: yes
Keywords: federated learning

clustered federated learning

privacy

distribution shifts

scalability

efficiency

unsupervised clustering

test-time adaptation
Abstract: Federated Learning (FL) enables collaborative model training across multiple clients while preserving data privacy. Traditional FL methods often use a global model to fit all clients, assuming that clients' data are independent and identically distributed (IID). However, when this assumption does not hold, the global model accuracy may drop significantly, limiting FL applicability in real-world scenarios. To address this gap, we propose FLUX, a novel clustering-based FL (CFL) framework that addresses the four most common types of distribution shifts during both training and test time. To this end, FLUX leverages privacy-preserving client-side descriptor extraction and unsupervised clustering to ensure robust performance and scalability across varying levels and types of distribution shifts. Unlike existing CFL methods addressing non-IID client distribution shifts, FLUX i) does not require any prior knowledge of the types of distribution shifts or the number of client clusters, and ii) supports test-time adaptation, enabling unseen and unlabeled clients to benefit from the most suitable cluster-specific models. Extensive experiments across four standard benchmarks, two real-world datasets and ten state-of-the-art baselines show that FLUX improves performance and stability under diverse distribution shifts—achieving an average accuracy gain of up to 23 percentage points over the best-performing baselines—while maintaining computational and communication overhead comparable to FedAvg.


Peer reviews:
comments: SUMMARY:
The paper introduces a method that efficiently handles various data distribution shifts during both training and test phases. The proposed method lies in its descriptor-driven clustering approach that extracts client-side descriptors through privacy-preserving feature extraction, followed by an unsupervised clustering algorithm to group clients with similar data distributions. The method was evaluated across multiple datasets, demonstrating good performance under various levels of heterogeneity and distribution shifts.

STRENGTHS AND WEAKNESSES:
This paper presents a well-structured and logically coherent work with good empirical performance. The authors provide a detailed report and experimental results on various heterogeneity and distribution shifts. However, several aspects require further clarification or improvement:

1.A table of contents should be added before the appendix to enhance readability and facilitate navigation for the readers.

2.The comparison methods used in the paper are relatively outdated. More recent methods should be introduced for comparison to better demonstrate the effectiveness of the proposed approach.

3.The performance of the proposed method is closely tied to the success of the unsupervised clustering process. When the clustering algorithm fails to adequately separate customers with different data distributions, the resulting model may underperform. This limitation warrants further exploration.

QUESTIONS:
None.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-21

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn Federated Learning (FL), dealing with data that is non-independent and identically distributed (non-IID) across clients can make generating a global model that accurately fits the overall distribution challenging. To tackle this issue, one approach is to shift the paradigm by grouping clients based on their local data distributions. This method is referred to as Clustered Federated Learning (CFL). However, many existing CFL approaches tend to focus only on specific types of distribution shifts, which limits their ability to provide a comprehensive understanding of how their methods perform under feature distribution shifts, label distribution shifts, and concept shifts.\n\nMoreover, most of these methods make unrealistic assumptions, such as knowing the number of clusters in advance or being able to assign test clients to existing clusters beforehand. To address these challenges, this paper introduces a novel cluster-based approach called Federated Learning with Scalable Unsupervised Clustering and Test-Time Adaptation (FLUX). \n\nFLUX assigns a descriptor to each client, which is constructed using class-conditional moments and can be enhanced with differential privacy to improve privacy guarantees without significantly sacrificing performance. These descriptors are then utilized to cluster the clients using a new technique inspired by DBSCAN, after which each cluster undergoes FL training. Extensive experiments demonstrate the effectiveness of this method.\n\nSTRENGTHS AND WEAKNESSES:\n### Strengths\n\n1) The effectiveness of the method is demonstrated through numerous experiments conducted on five datasets, comparing it with several baseline approaches.\n\n2) FLUX does not require prior knowledge of the exact number of clusters.\n\n3) The paper effectively addresses four types of distribution shifts in the experiments by investigating each one separately.\n\n4) The overall description of the method is convincing, and the motivations are generally well discussed, although there are some exceptions (see the weaknesses and questions).\n\n### Weaknesses\n\n1) The main paper provides a high-level overview of the method, while the specific details on how the individual components have been implemented are found in the Appendix. Although this choice is likely due to page limits, the authors might consider including additional details to clarify the method in the main paper while removing repetitions (e.g., some symbols have been introduced multiple times, or the entire background section could be simplified). Currently, the description can be somewhat confusing, making it difficult to understand the ideas behind its construction.\n\n2) FLUX requires all the clients to be available in every round, which could be an unrealistic assumption, especially in cross-device FL scenarios.\n\n3) Although the paper already provides a discussion on the efficacy of FLUX with various numbers of participating clients, experiments with a large number of clients (>1000) or more real-world problems (e.g., images with higher resolution) are missing. For instance, see the datasets proposed in [1].\n\n[1] Hsu, Tzu-Ming Harry, Hang Qi, and Matthew Brown. \"Federated visual classification with real-world data distribution.\" Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part X 16. Springer International Publishing, 2020.\n\nQUESTIONS:\n1) This paper is based on the assumption that traditional FL methods rely on the idea that clients' data is IID, which is often not the case. Indeed, this is a key distinction between Distributed Learning (DL) and FL, as noted in [2], Table 1. Furthermore, several studies have addressed this specific issue within FL, including works already cited by the paper (e.g., SCAFFOLD). Could the authors provide a convincing justification for this claim?\n\n2) How is Eq. 2 derived?\n\n3) How does FLUX relate to the method proposed in [3]?\n\n[2] Kairouz, Peter, et al. \"Advances and open problems in federated learning.\" Foundations and trends® in machine learning 14.1–2 (2021): 1-210.\n\n[3] Licciardi, A., et al. \"Interaction-Aware Gaussian Weighting for Clustered Federated Learning.\" Forty-second International Conference on Machine Learning (ICML), 2025.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents FLUX, a Clustered Federated Learning (CFL) framework designed to address data heterogeneity by leveraging client side data descriptors to form clusters in an unsupervised manner. FLUX can handle four data distribution shifts and train specialized models for each client group without needing to know the number of clusters in advance. Also, the framework supports assigning at the test time new and unlabeled clients to the most suitable model. Experiments show that Flux achieves good performance with minimal computational and communication overhead.\n\nSTRENGTHS AND WEAKNESSES:\n***Strengths***:\n\n(1) The paper is presented very well and all the sections build upon one another, making it easy to read the papers\n\n(2) The integration of the different components to make FLUX functional is impressive. \n\n(3) Due attention has been paid to both the communication and computation cost and the tradeoffs are well justified\n\n***Weaknesses***:\n\n(1) It is not clear what is the novelty in the the proposed method. In a sense, I do not know if the prime contribution is formulating a new way to do clustering or is it more of a system the ties in already established techniques\n\n(2) In Figure (11) it is seen that for more challenging datasets such as CIFAR-10 and CIFAR-100, the performance of FLUX is not the best across different heterogeneity levels. Thus, it would be good to have some explanation on this observation.\n\n(3) In Figure 5, the drop in performance with device scaling for FLUX is not explained\n\n(4) It will be interesting to see an experiment (ablation study) that compares test time performance with random assignment of client to a cluster vs using the assignment protocol as mentioned in FLUX\n\nQUESTIONS:\n(1) During training how were the different types of distribution shifts samples distributed across the clients?\n\n(2) Is there any investigation done to evaluate how the quality of descriptor degrades when amount of data available across each client changes?\n\n(3) If there are ties during cluster assignment, how is it resolved?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a federated learning framework (FLUX) that leverages privacy-preserving client descriptors and unsupervised clustering to group clients, aiming to handle arbitrary distribution shifts without prior knowledge of cluster number or type. It supports\ntest-time adaptation for unseen, unlabeled clients and demonstrates empirical improvements over several baselines.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The paper reports improved accuracy across multiple benchmarks and a real-world dataset, with overhead claimed to be comparable to FedAvg.\n\nWeaknesses:\n\n- The core idea—clustering clients for personalized federated learning—is not new and is wellestablished in the literature (e.g., MCFL [2] and FL-HC [1]). In FL-HC the server uses hierarchical clustering but the key idea is the same. The current paper uses DBSCAN where the radius epsilon is computed dynamically but the number off points per cluster is still the hyper-partameter. One-Shot Clustering is also explored in the context of unsupervised methods LADD [3].\n\n-  Lacks theoretical analysis of clustering such as impact of incorrectly clustered client and generalization.\n\n- The experiments on feature heterogeneity datasets such as DomainNet/Office-Home are missing.\n\n[1] Briggs, Christopher, Zhong Fan, and Peter Andras. \"Federated learning with hierarchical clustering of local updates to improve training on non-IID data.\" In 2020 international joint conference on neural networks (IJCNN), pp. 1-9. IEEE, 2020.\n\n[2] Long, Guodong, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, and Jing Jiang. \"Multi-center federated learning: clients clustering for better personalization.\" World Wide Web 26, no. 1 (2023): 481-500.\n\n[3] Shenaj, Donald, Eros Fanì, Marco Toldo, Debora Caldarola, Antonio Tavera, Umberto Michieli, Marco Ciccone, Pietro Zanuttigh, and Barbara Caputo. \"Learning across domains and devices: Style-driven source-free domain adaptation in clustered federated learning.\" In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp. 444-454. 2023.\n\nQUESTIONS:\n- Could you clarify the significance of the Distribution Fidelity in Eq. 4? (difference between the euclidean distance and Wasserstein distance)? As the Wasserstein distance is computed between two distributions which is unknown, it is unclear how this metric can be used. In the appendix this is provided assuming Gaussian distribution which may or may not always hold.\n\n- Could the authors explain how does the design of the descriptors contribute to fulfilling the Distribution Fidelity requirement? This is not clear from the explanation in the appendix (908-926).\n\n- What are the key advantages of using DBSCAN over Hierarchical Clustering as in FL-HC[1]? Specifically, how does DBSCAN improve upon the hierarchical clustering in this context?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 1, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a clustering-based federated learning framework, FLUX, which aims to address potential distribution shifts during both training and testing phases without relying on any prior knowledge. FLUX leverages a privacy-preserving descriptor extraction mechanism on clients to enable efficient unsupervised clustering and representation compression with low communication overhead. This design ensures both robustness and scalability of the global model, while enabling adaptive model selection for newly joined unlabeled clients during the testing phase. Extensive experiments on several benchmark datasets and real-world medical data demonstrate that FLUX outperforms 10 state-of-the-art baseline methods under varying degrees of data heterogeneity and task configurations.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\ni. This paper proposes a label-free model assignment mechanism for the inference phase, addressing a key limitation of many clustered federated learning (CFL) methods that struggle to generalize effectively on the client side during testing.\n\nii. Comparisons were conducted across five datasets, eight levels of heterogeneity, four types of distribution shift, and both seen and unseen testing conditions.\n\niii. The paper follows a well-structured and readable format.\n\nWeaknesses\n\ni. The motivation of the paper is not sufficiently substantiated. Although it highlights that many existing methods rely on prior knowledge, it lacks a more systematic categorization or statistical evidence to convincingly support the claim that \"prior-free\" approaches offer broader generality.\n\nii. The experimental section provides insufficient explanation of FLUX-prior. Although this variant appears frequently in tables and figures, its definition and distinction from the main method are not systematically clarified.\n\niii. The description of the clustering method in the paper is overly brief; it only mentions \"density-adaptive clustering\" without specifying whether it refers to DBSCAN or another algorithm, and lacks details regarding the parameters used.\n\nⅣ. The applicability of hard versus soft clustering remains unclear. While the paper adopts a hard clustering strategy, it does not explicitly justify this choice or compare its effectiveness against soft assignment approaches.\n\nⅤ. The paper does not clearly articulate the underlying mechanism of the proposed approach, and it lacks illustrative examples that demonstrate how the method effectively tackles the identified challenges.\n\nⅥ. The text in Tables 1 and 2 is too small and may affect readability.\n\nQUESTIONS:\ni. Has the performance of the descriptors been evaluated under varying levels of data noise?\n\nii.  How can the authors explain the subpar performance of the proposed method in certain cases?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-02"}