Title: FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Venue: NeurIPS 2025
Accepted: yes
Keywords: Federated Learning

Generalized Category Discovery
Abstract: Federated Generalized Category Discovery (Fed-GCD) aims to train a global model that classifies seen classes while discovering novel ones from data distributed across heterogeneous clients.
Existing GCD methods often rely on unrealistic assumptions, such as prior knowledge of the number of novel classes or balanced class distributions across clients.
We propose Federated Local Prior Alignment (FedLPA), which eliminates these assumptions by grounding learning in client-specific structures and aligning predictions with locally derived priors.
Specifically, each client constructs a similarity graph refined with high-confidence signals from seen classes, and then identifies local concepts and prototypes via Infomap clustering.
Building on these discovered structures, we introduce Local Prior Alignment (LPA), a self-distillation mechanism that aligns batch-level predictions with empirical class prior derived from concept assignments.
Through iterative local structure discovery and adaptive prior refinement, FedLPA achieves robust generalized category discovery under severe data heterogeneity.
Extensive experiments demonstrate that FedLPA significantly outperforms existing federated GCD methods across both fine-grained and standard benchmarks.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes FedLPA, a framework for federated generalized category discovery (Fed-GCD) that avoids common unrealistic assumptions such as known novel class counts or uniform class distributions. The idea is to build client-specific similarity graphs enriched with pseudo-labels, apply InfoMap clustering, and align local model predictions with discovered local priors using a self-distillation-based regularizer. Experiments on several benchmarks under non-IID conditions are conducted to evaluate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\nFed-GCD is underexplored yet important for real-world federated learning scenarios.\n\nWeaknesses:\n1. No theoretical analysis is provided regarding why the proposed local prior alignment scheme should converge or be stable across heterogeneous clients.\n2. The model still relies on exchanging model parameters frequently. There’s limited discussion on communication cost or privacy guarantees (e.g., can prototype leakage reveal sensitive information?).\n3. The title includes \"Local Prior Alignment,\" which sounds Bayesian, but no formal Bayesian formulation or interpretation is provided.\n\nQUESTIONS:\n1. The term \"Local Prior Alignment\" and use of \"prior\" throughout the paper give the impression of a Bayesian framework, but the method uses an empirical cluster distribution over batches (i.e., a histogram-based estimate). Can the authors clarify why they use the term \"prior\" in a non-Bayesian setting?\n2. The paper lacks theoretical insights or guarantees, particularly concerning the stability and convergence of the self-distillation and alignment mechanisms across heterogeneous clients. Can the authors provide any theoretical or empirical analysis (even simplified) on the convergence behavior or alignment stability across clients? Have you observed cases where the learned cluster structures diverge across rounds?\n3. Although the method avoids sharing raw data or prototypes, communication efficiency and privacy implications are largely unaddressed. What is the communication cost per round compared to FedAvg and AGCL? Could any of the learned local graph structures or soft predictions leak sensitive information? Have you considered adding differential privacy or gradient compression techniques?\n4. The similarity graph used for InfoMap clustering is derived from features produced by an under-trained model early in training, which could lead to noisy clusters and unstable learning. Do you have quantitative or qualitative results showing how graph quality evolves across rounds? Have you considered alternatives like initializing from a pre-trained encoder (other than DINO), or delaying the clustering process until later rounds?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis article considers generalized category discovery (GCD) under heterogeneous federated learning settings and proposes a novel self-distillation strategy. The FedLPA framework leverages the synergy between category structures identified through clustering and dynamic prior adaptation, enabling robust generalized category discovery under severe data heterogeneity. Additionally, this proposed framework successfully overcomes these unrealistic assumptions by adaptively estimating the category structure at the client level, significantly enhancing the practical value. Extensive experiments further demonstrates its performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths.\n1. The paper is technically sound, with a well-motivated and novel approach. \n2.  The paper is well-organized and clearly written.\n3. This work addresses the critical challenges of generalized category discovery (GCD) in federated learning. By proposing a method that relaxes unrealistic assumptions common in prior work, it pushes the field towards more realistic application scenarios. \n4. The core contribution—the Confidence-guided Local Structure Discovery (CLSD) pipeline and the Local Prior Alignment (LPA) regularizer is a novel idea for the Fed-GCD setting. Built upon existing concepts like graph-based clustering and self-distillation , the FedLPA framework is a well-crafted and original combination of techniques for this specific problem.\n\nWeaknesses.\n\n1.\tThe method requires each client to construct a similarity graph and run InfoMap clustering on all local data during communication rounds. You mentioned that more frequent updates (R = 1) yield better results. However, for clients with large amounts of data or limited computational resources, this could pose a significant computational bottleneck. The paper does not quantify this overhead or discuss potential optimization strategies.\n\n2.\tYou have selected JSD (Jensen-Shannon Divergence) in the LPA regularizer term. This is a reasonable choice as it is symmetric and bounded. I wonder if you have tried other divergence measures (e.g., simple KL divergence)? Is there any particular theoretical or empirical reason for choosing JSD? More details would be helpful.\n\n3.\tCLSD relies on the classifier trained during the warm-up phase. Could you elaborate on how sensitive this method is to the quality of warm-up? For example, if the warm-up is shortened to fewer rounds, how much would the overall performance degrade? This would help in understanding the robustness of the framework.\n\nQUESTIONS:\npls refer to the weakness part.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work tackles the challenging problem of generalized category discovery in federated learning (Fed-GCD). The main challenges mainly come from data heterogeneity (each client sees a different subset of classes with varying distributions), unknown and non-uniform category counts, and imbalanced class distributions. To address these challenges, the authors propose FedLPA, a client-side framework that discovers local category structures via graph-based clustering and enhances representation learning through a self-distillation strategy called Local Prior Alignment (LPA). By aligning model predictions with dynamically estimated local priors, FedLPA adapts to each client’s unique data distribution without requiring global class counts or balanced data. Experiments show that FedLPA significantly outperforms existing Fed-GCD approaches.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This work removes some unrealistic assumptions (e.g., a known number of novel classes, balanced distributions) commonly used in the open-world learning research field, making the Fed-GCD problem setting more realistic.\n2. The method contains two novel modules, CLSD and LPA, which guide self-supervised learning using local graph structure and batch-level priors.\n3. Rich experiments show that the proposed method outperforms state-of-the-art Fed-GCD baselines on both fine-grained and generic datasets.\n\nWeaknesses:\n1. Local clusters are not aligned across clients. For example, the discovered novel class \"A\" on one client has no correspondence to \"A\" on another client, which can be problematic, especially for novel class discovery.\n2. This method heavily relies on the quality of local clustering. However, in this federated learning setting, a few clients may encounter noisy features or samples. Under this condition, the quality of local clustering for some clients can be quite poor, which can negatively impact the performance of the global model.\n\nQUESTIONS:\n1. Could global novel-class prototypes be gradually learned via communication of aligned clusters or prototypes?\n2. How sensitive is FedLPA to noisy pseudo-labels or graph errors in the early training stage (when the quality is not good enough)?\n3. How does FedLPA compare to pure federated contrastive learning methods? Since FedLPA does not optimize the global novel-class prototypes via federated learning and mainly focuses on the structure of the embedding space, it is worth comparing with the federated contrastive learning methods, which also mainly focus on the structure of the embedding space.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n- The authors propose a federated learning (FL) algorithm for the Generalized Category Discovery (GCD) setting, dubbed as FedLPA, where partial data samples are unknown (i.e., the ground-truth label is not provided in the overall dataset) and unlabeled (i.e., the label of the sample is not given).\n- The key part of FedLPA is the Local Prior Alignment (LPA) loss term, which combines two terms: a contrastive perspective term (comparing an unlabeled sample and its augmented one) and the distributional divergence term (comparing the computed logit distribution and its empirical prior).\n- Through the extensive simulation results, FedLPA outperforms baselines with the extensions of the GCD methods to the FL setting. Also, the ablation studies have shown that the LPA term brings substantial performance gains.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength 1: Convincing and timely extensions of the GCD problem to the FL setting**\n- The main strength of this paper is demonstrating the technical improvement in the convincing and timely combinations between GCD and FL. In the future, it is anticipated that a deep model will undergo lifelong training in distributed agents, enabling it to discover novel concepts or categories. With this perspective, as done in this paper, the extensions of GCD to FL and the technical advances are meaningful steps in the related research society.\n\n**Strength 2: Extensive simulation results and the substantial performance gains**\n- The proposed method, i.e., FedLPA, has been extensively tested on various datasets, ranging from simple ones such as CIFAR and CUB-200 to more complex ones, including ImageNet, Stanford-Cars, and Oxford-Pet. I am well convinced that the proposed method is truly effective in the various FL+GCD scenarios.\n- In addition, the performance gains are quite substantial beyond the baselines. For example, in CIFAR-10, the gains exceed +10%, and this trend is consistent across all cases. I believe that the gains are meaningful in realizing a practical FL method successfully working in the GCD scenario.\n\n**Weakness 1: Limited insights into the broader impact of the FL+GCD topic**\n- To my understanding, the key contributions of this paper are focused on technical improvements for FL+GCD. The authors have employed delicate techniques, including precise procedures for local structure discovery, clustering, and the combination of various loss terms (including the LPA term). \n- From this perspective, it is difficult to identify the novelty of this work, particularly in envisioning how FL+GCD should be. When focusing on the key part of this work, i.e., LPA, the authors should provide concrete evidence: **i)** Why should we compare the logit distribution and the empirical distribution (referring to the second term in Eq. 3)?, **ii)** How does the term relieve the challenges in FL+GCD (when referring to the limitations of the existing approaches listed in the Introduction)?, and **iii)** Any broader impacts and applications of this loss term design to other FL+GCD approaches?\n- To my understanding, other technical parts, including local graph structure discovery and InfoMap, can be viewed as borrowed and assembled parts from the well-known prior works (we know that graph construction and revision are widely used in transductive and semi-supervised approaches. For example, Tranductive Propagation Net is a method to find/revise the graph of novel samples, which is a clear example [TPN'18]). Thus, I hope to narrow my viewpoint to the LPA part when assessing the technical novelty.\n- However, the technical design of LPA is shown, but the reasoning for this design, the role in relieving the challenges of FL+GCD, and possible extensions of LPA have not been addressed.\n\n[TPN'18] Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning, ICLR'19\n\n**Weakness 2: Limited discussion on convergence (both in the empirical and theoretical viewpoints)**\n- In the FL society, it is crucial to provide a convergence guarantee for the newly proposed FL algorithm. However, in this paper, I cannot find any evidence of the FedLPA's convergence, either from the empirical or theoretical viewpoints.\n- When a FedAvg is done with the LPA loss, is it possible to provide a theoretical convergence analysis? Also, if the theoretical analysis is too challenging, is it possible to provide the convergence behavior of FedLPA across rounds, `at least' via empirical results?\n- For more comments on the empirical investigation, I hope to know how fast FedLPA converges across rounds. For example, does FedLPA show a reasonable convergence when compared to other baselines? I am quite worried that FedLPA shows a slow convergence due to the highly delicate design of various loss terms. It would be beneficial to see the required rounds to saturate for each algorithm in Table 1 and 2.\n\nQUESTIONS:\nMy main questions are based on Weaknesses 1 and 2. \n- Weakness 1 is based on the concern that contributions are highly focused on the technical improvement, rather than showing the insights: why we use LPA, how LPA relieves the challenges of FL+GCD, and further impacts on other FL+GCD methods.\n- Weakness 2 is based on the limited analysis of convergence, either in theoretical and empirical analysis.\n- Please refer to the Weakness part.\n\nAdditional minor questions are as follows:\n- I am not sure how to compute the empirical prior $\\pi_{n, B_u}$ in the LPA term (Eq. 3). For unlabeled or unknown samples, we do not know the actual empirical distribution. Thus, I wonder how to compute the empirical prior. To the best of my understanding, it would be possible to compute the empirical prior of the known/labeled samples while leaving the prior of unlabeled or unknown samples vacant (or equally-distributed). However, I am not sure the exact process. Please elaborate on the computation of $\\pi_{n, B_u}$.\n\nLIMITATIONS:\nThe authors have identified some limitations, such as adaptive client sampling and asynchronous updates, which are common challenges encountered in practical FL systems. These are convincing comments when focusing on the practicality of FL.\nHowever, my additional comments on the limitations are based on the essential understanding of the proposed method, and in-depth analysis, as aforementioned in Weaknesses 1 and 2. Please refer to Weaknesses 1 and 2.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}