FedLPA: Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Authors: Geeho Kim
Jinu Lee
Bohyung Han
Venue: NeurIPS 2025 poster ()
Keywords: Federated Learning
Generalized Category Discovery
Primary area: infrastructure

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.

FedLPA Local Prior Alignment for Heterogeneous Federated Generalized Category Discovery
Geeho Kim
Jinu Lee²
Bohyung Han
ECE & IIPAI, Seoul National University
Toss (Viva Republica)
(snow1234, Ahhann@smi ac.kr
jinu wew.toss.sss... im
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 the number of novel classes or balanced class distribu- tions across clients. We propose Federated Local Prior Alignment (FedI.PA), 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-distillatio 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 Fedl IPA signifi- cantly outperforms existing federated GCD methods across both fin-grained and standard benchmarks.
Introduction
Machine learning models increasingly operate in open-world settings where not all classes are available during training. Generalized Category Discovery (GCD) [22, 3] addresses this challenge by categorizing unlabeled data containing instances from both seen and novel classes, leveraging knowledge from the labeled examples. However, existing studies on GCD have predominantly focused on centralized settings, assuming universal access to training data, which comprises two types of classes-ss seen and novel-with annotations available for only subset of the seen classes. In such settings, the number seen classes is known, and the number of novel classes is typically assumed to be given. This centralized formulation, however, overlooks more practical scenario where both data and computational resources are distributed across multiple clients.
We address the Federated Generalized Category Discovery (Fed-GCD) problem, which extends GCD to the federated learning setting. this scenario, each local client independently maintains its own training data without sharing it with others due to privacy constraints [15]. While the core objective of Fed-GCD aligns with that of centralized GCD, training within a privacy-preservin
federated learning framework introduces additional challenges due the following reasons. First, each client encounters more severe data heterogeneity and class imbalance [9, 8, 1]. as training examples are partitioned across individual clients. Second, and more critically, the sets of classes may differ across clients. In other words, unlike the prevalent assumption in centralized GCD [3, 27, 23, 25, 18, 29], each client observes only a partial class set of the global label space; consequently, the number of
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

novel classes varies across clients and is unknown at the client level, while the total number of novel classes over all clients is also unknown.
Existing centralized GCD approaches often rely on assumptions that are unrealistic in the federated learning setting. First, most methods require prior knowledge of the total number of novel classes [3, 27, 23, 25, 18, 29] to configure their classifiers and loss functions. Second, these methods often assume uniform class distributions. For instance, classifier-based approaches such as SimGCD [27] and its variants [25, 23] employ entropy regularization to encourage balanced predictions across all classes (both seen and novel), while clustering- based methods [20, 16, 30] impose cluster-size balance constraints to ensure well-separated novel class clusters. Even recent federated methods inherit these assumptions; for example, FedoSSL [28] presumes knowledge of the total number of novel classes across all clients, while AGCL [19] similarly assumees uniform cluster distributions. These assumptions directly conflict with the inherent data heterogeneity and class imbalances prevalent in Fed-GCI.
To overcome these limitations, we propose a novel federated learning framework that eliminates such unrealistic assumptions by discovering data structure at the client level. Rather than requiring prior knowledge of the global number of novel classes, our approach enables each client to construct local similarity graph over its entire local dataset, comprising both labeled and unlabeled data, by incorporating mround-truth labels and high-confidence pseudo-labels assigned to unlabeled examples. Applying Infomap clustering to this graph reveals client-specific class priors including the cardinality of local novel classes.
Building on these discovered concept structures, we introduce Local Prior Alignment (LPA), a novel self-distillation strategy that enhances generalized category discovery under skewed local data by aligning the model's batch-level predictions for unlabeled examples with the locally-derived concept distributions. This simple mechanism effectively guides the model toward each client's true data struc- ture, enabling robust representation learning across heterogeneous clients. The proposed approach demonstrates remarkable performance improvements across all datasets and settings consistently, outperforming existing BcdGCDD baselines by significant margins.
Our main contributions are summarized as follows.
We propose FedLPA, novel federated learning framework for generalized category dis- covery that eliminates the need for prior knowledge of novel class counts while naturally accommodating non-i.i.d. data distributions across clients.
We establish a graph- baspd category discovery mechanism that constructs local similarity graphs enriched with high-confidence seen-class signals, and applies Infomap clustering to derive client-specific class priors and concept prototypes.
We introduce Local Prior Alignment (LPA), a self-distillation strategy that adaptively regularizes batch-level predictions by aligning them with empirical local priors, thereby enhancing robustness under severe data heterogeneity.
FedLPA demonstrates its outstanding performance in terms of robustness client hetero- geneity on fine-grained and standard object recognition benchmarks under various settings.
In the rest of this paper, we first review related works in Section and discuss our main algorithm in Section 3. Section 4 presents our experimental results and Section 5 concludes this paper.
2
Related Works
2.1 Centralized generalized category discovery
The objective of Generalized Category Discovery (GCD), formulated by [22, 3], aims to classify samples from seen categories while simultancously discovering novel classes, leveraging knowledge from labeled data. Unlike Novel Class Discovery (NCD) [7], which assumes unlabeled data contains only novel classes, GCD addresses more realistic and challenging scenario, where unlabeled data includes both known and novel classes. Existing GCD approaches typically adopt two main paradigms: parametric classifier learning and non-parametric representation learning.
Parametric methods [3, 27, 23, 25, 26, 14] build a learnable classifier and optimize using labeled data and pseudo-labeled data generated from model predictions. These methods incorporate adaptive

---

margins [3] or entropy regularization [27, 23, 25, 14] to achieve balanced gseudo-labelingg and leverage mean-teacher frameworks [23] prompt-tuning [25] refine pseudo-labed quality. In contrast, non-parametric methods employ contrastive learning with diverse Ktraegies--combine losses [22], multiple projection heads [6], hierarchical [20] or concept-level [18] formulations, or GMM-based generative sampling [30]-t0 enhance feature generalization to novel categories. However, these approaches focus on centralized settings, and rely on assumptions ill-suited for realistic federated learning settings. First, they often assume prior knowledge of the number of novel classes [3, 27, 23, 25, 20, 18], or require labeled validation data to estimate the class counts [22, 18, 20, 6]. Second, they frequently make the stronger assumption that class distribution is balanced. For instance, parametric methods [27, 16, 23, 25] commonly adopt mean entropy maximization (ME- MAX [2]) to enforce uniform predictions across all classes, while non con-paametric approaches [20, 16, 30] impose cluster-size balance constraints to maintain clusters corresponding to novel classes. Such assumptions often impractical in real-world distributed settings, where data is partitioned heterogeneously across clients.
2.2 Federated generalized category discovery
To address the limitations of centralized approaches, recent work has explored Federated Generalized Category Discovery (Fed-GCD). Fed-GCD extends GCD to the federated learning paradigm [15], where clients collaboratively train global model without sharing their raw data, thereby preserving privacy. The objective of Fed-GCD is training a global model that discovers novel categories and accurately classifies known categories distributed across heterogeneous clients. This task is more challenging than centralized GCD, as each client observes only a partial class set of the global label space due to severe data heterogeneity. Early work, FedoSSL [28] tackles heterogeneity novel class distributions by distinguishing locally unseen classes (novel in some clients) from globully unseen classes (novel across all clients). However, FedoSSI. assumes the total number of novel classes known priori and that seen class data is i.i.d. and balanced across clients. More recently, AGCL [19] addresses Fed-GGD under more realistic settings, where both seen and novel classes exhibit highly skewed and non-i.i.d. distributions, employing GMM-based contrastive learning to learn robust representations of both seen and novel classes. However, AGCL assumes a uniform cluster prior and samples accordingly, which leads to mismatch with the true local data distributions. Additionally, it requires clients to send local class representations to the server, raising privacy concerns and incurring additional communication overhead. In contrast, our framework overcomes these limitations through client-level structure discovery and adaptive prior alignment, without assumption of balanced class distributions while preserving privacy.
3 Proposed Algorithm: FedLPA
This section presents our approach for federated generalized category discovery, referred to as FedLPA, which combines graph-based local category discovery and adaptive prior regularization.
3.1 Problem setup
We consider a federated learning setting with N clients C = [ Each client Cn maintains a local dataset D, DD. DH, partitioned into labeled and unlabeled subsets. The labeled subset
D', {(x,, v) ) i=1 contains instances with known labels y/i E yM, while the unlabeled subset DH [x IPH contains instances whose true labels belong to 2% but remain unobserved. The
[
- D and the global label space (encompassing global known label set is defined as ) U
both known and novel classes) is y = - Dhh with y S ) Novel classes correspond to
g" \ yd and appear exclusively in the aggregated unlabeled data D = U,, Du The objective of Fed-GCD is to collaboratively train global model X' + ) from {D,)) that accurately classifies unlabeled instances into their true classes in ) Crucially, client data distributions are
heterogeneous- label sets 2% and y vary across clients, and the cardinalities [)**| and |) are unknown a priori. Moreover, raw data remains local to each client and cannot be shared due to privacy constraints.
3

---

O: Labeled C:
Unlabeled
Seen classes
Novel class
Execute every R - Every iteration
(a) Confidence-guided Local Category Discovery (CLCD)
(b) InfoMap clustering
Unlibiee wen
Unibbeled novel
g
Cyniblenes Confidence-quider Identification
Initial graph
Graph refinement
Discovered concepts 5%
(c) Local Prior Alignment (LPA) with self-distillation
Initialize local prototype set M
Botman
P1
JSD (p (TT
P B
swagmis sloves
M
Elgl.
LPA
Softmas
9
Stop grad
uif fi.m
Sothmay
C
D Seo
Figure 1: Overview of FedLPA's local training process. (a) Every R rounds, each client constructs local similarity graph using the current global backbone o' and refines with (pseudo-) >labels from seen classes. (b) Fach client applies graph-based clustering (Infomap) on the refined graph, and obtains a local category prior V and corresponding concept prototypes M (c) During local training on unlabeled data, self-distillation minimizes cross-entropy between predictions p; and soft targets of derived from prototype similarities in M Concurrently, Local Prior Alignment (LPA) aligns the batch-averaged predictions РB., with the batch-specific empirical class prior Tn,B. via Jensen-Shaie Shannon Divergence (JSD). For labeled data, the framework employs standard cross-entropy and supervised contrastive loss, while unlabeled data additionally uses unsupervised contrastive loss.
3.2 Overview
FedLPPA achieves robust generalized category discovery under severe data heterogeneity and class imbalance without requiring prior knowledge of novel class counts or imposing class-size balance assumption. As illustrated in Figure we accomplish this through three synergistic stages at each client: (1) constructing an enriched local similarity graph via Confidence-guided Local Category Discovery (CLCD), (2) applying Infomap clustering to derive client-specific class priors, and (3) employing Local Prior Alignment (LPA) to align model predictions with these discovered local priors. This integrated approach enables robust category discovery across heterogeneous clients by grounding learning in each client's 's true data structure.
FedLPA follows the standard federated learning framework, FedAvg [15]. Specifically, a central server initializes global model parameterized by (Ø) v)), comprising a feature extractor f(:; and classifier g(: Φ)) dedicated seen classes y". At each communication round {1,.... T)) the server distributes the global model 0' to an active client subset C, C C. Each client C₂ E C. initializes its local parameters (h_ 0' and performs M local optimization iterations its local data D,,- The server then aggregates the resulting local models 6 and updates the global model gt-1 for the next round of training by averaging the local model parameters. This training process is repeated until the global model / converges.
3.3 Confidence-guided local category discovery
Initial similarity graph construction Fach client constructs an initial similarity graph G., (In, E encoding pairwise feature relationships among its local samples D,.. The node set I,

---

comprises all local samples 2; E Dr- Each edge weight Ejj E En is the cosine similarity between ez-normalized feature embeddings v and V/, extracted using the global backbone f(=: o³) for samples Iy and 2, respectively. This initial graph Gn establishes foundational structure capturing pairwise feature affinities within the local data.
Confidence-basee known sample identification Since the initial graph relies solely on feature similarity, it is susceptible to noise and may not accurately reflect semantic relationships. To mitigate this, we refine the graph G,, by incorporating supervisory signals from known categories ) identifying high-confidence pseudo-labels within the unlabeled set ". Specifically, we utilize the global model 0 = {d', v* received at the start of round / (i.e., 0%,o) to compute logits h(x; 0*)
(f ););;)))) over the seen classes }" For each unlabeled sample 2i, we define confidence score (I;;) as the maximum softmax probability derived from these logits. If s1x;) exceeds a client- specific threshold f.,. determined the P-th percentile of confidence scores on the local labeled
data D' we
we consider a reliably identified known sample. We assign pseudo-label 0s E ) corresponding to the class with the highest probability, collecting these samples into set:
Dyt.seen = [(a,,, 96) az E DH, s(x.) > En, and fs- = arg max o (1((2;;;)) )) n
(I)
kEY"
where (-)) denotes the softmax function. To ensure reliable confidence estimates, particularly in early rounds, we employ an initial warm-up training phase. Further details on the warm-up procedure and the determination of En are provided in the supplementary document.
Label-informed graph refinement Leveraging the (eeuud-)labbled sample set, each client refines the edge weights E, of its local similarity graph G,, to explicitly encode these supervisory signals. Let ". = D' U syeseen be this set of samples with (pseudo-) labells DJ y We recalibrate the initial edge weights Eij E En by imposing the following rules:
1,
if F3z 23 E Di,,, Ús = Ūj. and + if 2z E D Ds # ūj. and i +
c
0,
Cijy
otherwise
To further enhance the graph's 's robustness against feature noise, we apply an edge-pruning mechanism. This step establishes the final edge set E, by filtering out edges with weights falling below a predefined threshold Ty. The resulting refined graph G², {In, E yields more discriminative topology, thereby facilitating the subsequent local category discovery.
3.4 Infomap clustering
To uncover the latent semantic structure within the local data, each client applies the Infomap algorithm [21] to the refined graph G Infomap partitions the graph into communities by minimizing the description length of a random walk. This topological partitioning allows us to derive set of concept assignments {c,} for each sample, thereby implicitly determining the number of discovered concepts Kn = I.
Based on these assignments, the client constructs set of K,, local prototypes, denoted as M ) Specifically, each prototype 1h. is computed as the centroid of the fz-normalized feature representations associated with concept cg- These prototypes serve as semantic anchors for the self-distillation mechanism described in Section 3.5. To accommodate feature drift during training. this entire discovery and initialization process is periodically re- re-executed every R communication rounds at the start of local training.
3.5 Local prior alignment (LPA) with self-distillation
Building upon the discovered local concepts, we introduce a robust self-distillation strategy aug- mented by principled regularizer, termed Local Prior Alignment (LPA). During local training, the unsupervised objective for client n on an unlabeled mini-batch 8" C B is formulated as:
1
CLPA
l(gl p.) ISD(P xx,B").
(3)
B"
B
5

---

This objective combines an instance-level self deffddistilltiinn loss ((--)) (cross-entropy" with the proposed LPA regularizer, balanced by the coefficient The precise definitions and mechanisms of these two components are detailed below.
Instance-level consistency The self-distilleee component refines feature representations by en- forcing predictive consistency across two augmented views, 2; and z/. of the same image. For the first view F,, the model computes soft prediction vector p; based on the cosine similarity between its feature embedding = f(z; 6% and the local prototypes M A sharpening operation with temperature is applied: p: x exp(sim((;; /)/T)) Simultaneously, target distribution of is gener- ated from the second view z using lower temperature Tt < Ts to produce a sharper supervision signal.
Batch-level aligmment The LPA regularizer acts distributional constraint, aligning the model's marginal prediction distribution with the inherent class distribution of the batch. We minimize the Jensen-Shannon Divergence (JSD) between the mean model prediction Pa> and batch-specific empirical class prior B". The prior distribution is derived from the frequency of pre-assigned concepts {cy} (obtained via Infomap) within the batch:
T [
Σ 2
I(e, = cb), E [K,],
1
where de denotes the l--th concept in D The model's marginal distribution is computed by averaging the soft predictions from both views:
DB
Σ I.EB"
2 + P().
(5)
B
By enforcing this alignment, LPA adaptively guides the model towards the client's 's true local data structure and ensures robustness against severe class imbalance.
3.6 Joint optimization
To facilitate robust representation learning, we employ supervised [10] and self-supervised [4] contrastive losses, formulated
exp (u. ('//To)
C
log
(6)
B
ΣiSs exp (v. (iTTt//T)
iEB!
gEN,
exp(v,
vl/TT)
Crep
log
B
Σ
exp(u (T//TM)
iEB
where Nú denotes all other samples in the batch with the same label as F,, and Te, Tu are temperature parameters. For labeled data, we apply standard cross-entropy loss L'g on the labeled batch B' to train the local classifier g(; vif vi.m)) for seen classes. Combining all components, the overall objective function for each client n
C. = XLTi.A + ) + (1 - A) + Crr)),
(8)
where À a hyperparameter balancing the loss terms. The detailed learning procedure of FedLPA described in Algorithm 1 in the supplementary document.
4 Experiment
4.1
Experimental setup
Dataset We evaluate our proposed method on six image classification benchmarks: three fine- grained datasets (CUB-200 [24], Stanford-Cars [1]], and Oxford-IIIT Pet [17]) and three generic object recognition datasets (CIFAR-10 [12], CIFAR-100 [12], and ImageNet-100 [5]). For each dataset, we designate half of the classes as known and the other half as novel. From the known classes, 50% of instances comprise the labeled training subset, while the remaining instances, along

---

with all instances from novel classes, form the unlabeled training subset. To simulate non-i.i.d data distributions across clients, we sample label proportions from a symmetric Dirichlet distribution with a concentration parameter a {0.2, 0.05], following [8, 19]. This procedure yields [C] = 5
client-specific subsets, where each subset serves as local dataset for an individual client.
Baselines We compare our method, dubbed as FedLPA, with the state- ohe-t--rttttttt Fed-GCD methods: GCL [19], and AGCL [19]. We further establish federated baselines by adapting prominent centralized unsupervised learning method, PCI. [13]. These methods are integrated with FedAvg [15] following the strategy in [19], and are denoted FedAvg + GCD, FedArg SimGCD, FedArg + GPC, and FedAvg PCL. respectively. For FedAvg + SimGCD, we assume the number of novel classes is known a priori to initialize the classifier.
GCD methods-GCI [22], SimGCD [27], and GPC [30]-as as well
Evaluation protocol We evaluate model performance using clustering accuracy (ACC) an unlabeled test set held by the server, following the standard practice in [19]. This test set, along with labeled validation set, is partitioned from global evaluation set, mirroring the partitioning scheme used for the training data. Note that the baselines [19, 30, 22, 13] utilize the labeled validation remi-supervised clustering. To ensure direct and
data for either category number estimation fair comparison with these baselines, we also report the performance a variant, FedLPA+, which utilizes this validation by applying our CLCD algorithm to guide semi-supervised clustering.
Given predicted labels ÿ and ground-truth labels yi, ACC is defined as follows:
No Σ 10: [[x)),
ACC = max HES., No
==1
where S₁ denotes the set of all possible permutations of & cluster assignments, N₂, is the total number of unlabeled test samples, and IT..) represents the optimal mapping found via the Hungarian algorithm. We report ACC for all unlabeled test samples ("All"), as well as separately for samples from "Old" classes (y/ E ) and "New" classes (y/ E y ())).
Implementation details We a ViT-B/16 pretrained with DINO as the backbone. We the output of the [CLS] token with dimension of 768 as the feature for an image, and only fine-tune the last block of the backbone, following [19, 22]. The model undergoes a warmup stage of 20 rounds, followed by 50 rounds of Fed-GCD training. Both stages use SGD with a batch size of 128 and an initial learning rate of 0.1. For Fed-GCD training. the learning rate is decayed via cosine schedule. Following [19], the number of local training epochs is set to I with full client participation. The balaneing factor is set to 0.35, the temperature values Ts Tex Tn are set to 0.1, 0.07, 1.0, respectively. Following [27, 22]. т, starts at 0.07 and anneals to 0.04 over the first 30 rounds using a cosine schedule, For FedLPA, we set the percentile P to 80, the LPA regularization weight e to 0.5, and the CLCD update frequency R as I. We set 7 to 0.6 and 0.4 for fine-grained and standard datasets, respectively. All experiments were conducted on a single NVIDIA RTX A6000 or A5000 GPU.
4.2 Results
We evaluate the proposed methods, FedLPA and FedLPA+, on six benchmarks, encompassing three fine-grained datasets and three standard object recognition datasets, under varying degrees of data heterogeneity. As detailed in Table and Table 2, both FedL.PA and FedL_PA+ consistently outperform all existing Fed-GCD baselines across all datasets at every data heterogeneity level. Notably, Fedl LPA achieves these gains without any server-side labeled validation data, a common prerequisite for the basclines. This demonstrates FedLPA's s robustness in realistic, resource-constrained federated settings, even when the server-side labeled validation data is not available. For direct comparison, FedLPA+ leverages the server-held validation data by applying our CLCD algorithm to the combined validation and unlabeled test sets, yielding further performance improvements in most cases. Among the baselines, FedAvg SimGCD often struggles struggles-particulee particularly on standard object recognition datasets likely due to its restrictive uniform-prior assumption. Similarly, FedAvg + GPC underperforms FedAvg PCL in most cases, as it enforces balanced cluster size. These observations indicate that assuming class or cluster balance is ill-suited for the non-i.i.d. and imbalanced Fed-GGD settings, highlighting the advantages of our adaptive, data-driven structure discovery mechanisms.
7

---

Table 1: Results fine- aine-grained datasets with two different degrees of data heterogeneity. Methods with a dagger i report results from [19]. The 'Server val.' column indicates whether server-side labeled validation data is used for evaluation. Bold black and plain red numbers indicate the best and second-best performance, respectively, in each column.
CUB-200 a 0.05
StanfonJ-Cars 0.2 All Old New All Old New
Oxford-Pel 0.2 Old New
Method
Server val.
<t 0.2
0.05
0.05
All Old New
All Old New
All
All Old New
FedAvg + GCD [22]
46.3 54.8 40.
43.3 52.8 38.9 32.4 49.8 28.3
30.4 46.1 26.5 76.2 77.8 75.2 72.1 76.4 71.5
FedAvg SimGCD [27]
36,8 30.4
48.5 27.7 35.1 56.3 24.9
30.3 43.9 23.7 43.6 39.7 45.6
36.7 34.1 38.1
FedAvg + PCL [13]
51.3 47.5
46.3 35.3 47.7 33.4
32.6 45.5 29.2 79.4 80.3 79.1 76.6 77.9 74.7
FedAvg + GPC [30]
49.1 47.0
44.7 34.1 45.5 32.6
30.9 45.3 27.8 78.8 78.5 79.1 73.1 77.3 73.5
FedAvg + GCL [19]
53.7 53.2
52.2 53.1 52.9 36.0 33.7
35.3 45.7 31.5 80.7 81.3 80.2
79.5 81.5 78.6
FedAvg + AGCL [19]
52.5 56.7
53.1 52.9 54.2 38.2 50.8 36.0
36.4 44,9 32.8 82.7 83,9 82.3
81.4 82.0 80.7
FedL.PA (ours)
62.3 61.8
63.1 60.1 52.1 67.6
44.6)
51.8
64.9
45.4
85.3 84.2)
83.3 86.6 81.5
FedLPA+ (ours)
63.5 63.6 63.4
62.6
64.3
61.8 57.7 70.1 51.7
54.2 69.4
46.8 86.7 90.7 84.7 85.0 86.9 84.0
Table 2: Results on standard object recognition datasets with two different degrees of data heterogene- ity. Methods with a dagger report results from [19]. The 'Server val.' column indicates whether server-side labeled validation data is used for evaluation. Bold black and plain red numbers indicate the best and second-best performance, respectively, in each column.
CIFAR-I0 a 0.05
CIFAR-I00
ImageNet- 100
Methods
Server val.
a 0.2
a = 0.2
= 0.05
a 0.2
a = 0.05
All Old New|
All Old New
All Old New/
Old New
All Old New) All Old New
FedAvg + GCD+ [22]
80.7 82.3 80.3 78.7 80.1 78.3 49.6 52.1 49.3 47.3
49.652.1
49.3 49.2 45.9 69.8 77.1 65.7 66.4 74.8 62.1
FodAvg + SimGCD [27]
53.6 53.6 52.9 66.2 46.3 43.1 57.0 36.2 33.6 40.1 30.4 54.8 77.1 43.5
43.4 62.2 33,9
FodAvg + PCL [13]
81.6 80.9
80.0 80.7 79.4 53.2 54.1 51.7
51.6 49.0 72.4 79.5 66.0
70.1 77.0 63.3
FodAvg + GPC [30]
81.7 80.5
80.1 80.4 78.4 53.8 51.4
50.0 48.9 72.1 78.2 65.7 69.8 76.8 63.1
FedAvg + GCI. [19]
84,2 82.8
82.4 81.9 54.1 55.7 54.0
52.1
53.2 51.9 74.1 81.8 67.3 72.5 79.8 65.3
FedAvg + AGCLT [19]
85.5 84.6
82.5
83.5
83.4 82.2 56.1 56.8
55.3
54,2
54.6 54.0 74.8 80.2 69.8
73.1 78.1 67.0
FedLPA (ours)
94.5 94.5
94.6
93399
94.5
93.7
57.7
61.7
55.7
54.2
60.7
50,9
75.9 89.7 69.0
87.6 65.9
FedT.PA (ours)
95.1 96.3 93.9
94.1 95.1 93.3
58.1
63.4
55.4
56.5
64,9 52.3 76.6 90.6 69.9
74.4 88.6 67.3
4.3 Analysis
Ablation study To validate the efficacy of individual components within our FedLPA framework, we conduct an ablation study on Stanford-Cars under non-i.i.d. settings (a = 0.2 and = 0.05), and the results are presented in Table 3. The results show that each proposed element contributes significantly to the final performance. Specifically, applying our Local Prior Alignment loss (Cipx) substantially enhances performance, even with fixed target prior derived once from an initial graph of local unlabeled data (row 1). Our proposed regularizer on adaptive prior, empirically computed from each local batch, yields further significant gains (row 2). Notably, even without the confidence- guided graph refinement, our framework, LPA loss with an adaptive prior (row 3 and row 4), already demonstrates strong performance, outperforming all compared algorithms in Table 1. This highlights the robustness of our core LPA mechanism. Furthermore, our proposed cefidenne-guider graph refinement provides an additional performance gain (row 5).
Analysis of CLCD algorithm To validate the effectiveness of our proposed CL.D algorithm, we ablate the clustering module by replacing with semi-supervised K-Means (used in [22, 30] and semi-supervised FINCH (used in [19]) within the FedLPA framework, and the results are presented in Figure It is important to note that for the implementation of both semi-supervised K-Means and FINCH, we assume that the knowledge of the true number of classes in both local data and the global test data is given. We report two outcomes: (a) clustering accuracy on each client's local data immediately after warmup (Figure 2a); and (b) final clustering accuracy on the server test set after 70 federated training rounds (Figure 2b). Our proposed method achieves superior clustering performance on local training data compared to the other algorithms. Consequently, this leads to more significant improvement in the final test accuracy of Fedl. .PA both on seen and novel classes, underscoring the effectiveness of the proposed CLCD algorithm on the overall model performance.
8

---

Table 3: Component analysis of the proposed methods in the non-i.id. settings Stanford-Cars with two different degrees of data heterogeneity (a = 0.2 and a = 0.05). Bold black and plain red numbers indicate the best and second- performance, respectively, in each column.
a 0.2
= 0.05
Pin
Target prior
Initial graph
CLCD
Old
New
New
34.1
50.8
26.0
32.0
50.3
23.1
Fixed Adaptive
D
61.7
41.8
47.3
55.5
43.2
D
51.4
66.1
44.3
50.5
61.6
45.1
Adaptive
D)
DE
54.4
67.9
47.2
52.3
66.4
45.4
Adaptive
2), UD
57.7
70.1
51.7
54.8
69.4
46.8
80-
65-
Samissup. KMeans
Sami sup. FINCH
Sumissup FINCH
CLSD with Infomap
CLSD with Inforap
60
99.3
594
70-
17.4
ACC (%)
63
ACC (%)
$1.0
60-
437
Client
Client
Client 2
Client 3
Client
Avg.
Old New
(a) Clustering accuracy on local data after warm-up
(b) Final clustering accuracy on server test data
Figure 2: Ablative results of CLCD algorithm in FedLPA under non-i.i.d clients (a 0.2) on Stanford-Cars. We evaluate (a) clustering accuracy on individual client local training data right after the warmup training rounds, and (b) final clustering accuracy federated training rounds. For the final clustering accuracy, all methods are evaluated identically at test time: we apply the same server-side Infomap clustering to the unlabeled test set with fixed pruning threshold (ry = 0.6), regardless of the clustering algorithm used during local training.
the server test after 70
Increased number of clients We validate our framework in more challenging scenarios with an increased number of distributed clients (N 10). All methods suffer from performance degradation, compared with the results in Table 4, due to the reduced local data per client, and increased data disparity. Despite these challenges, FedLPA consistently shows promising performance all tested datasets.
Hyperparameters We investigate the impact of our hyperparameters on the performance of FedLPA under non-i.i.d. setting with 0.2, and the results are presented in Figure 3. Both the CLCD identification percentile P (Figure 3a) and the Local Prior Alignment (LPA) regularization weight (Figure 3b) demonstrate robust performance across reasonable range of values, indicating FedLPA's stability. For the CLCD update frequency R (Figure 3c), while more frequent updates (Rt = 1) yield better results by enabling rapid adaptation, FedL.P maintains competitive accuracy even with sparser updates. This offers valuable trade-off, allowing for reduced computational overhead with only marginal performance decrease, beneficial in resource-constraing number of warmup rounds (Figure 3d), while a marginal performance drop is observed with very few initial rounds, FedLPA rapidly achieves competitive accuracy with a modest number of rounds (e.g.. 10-20). The performance generally exhibits an upward trend and stabilizes as the number of warmup rounds increases (e.g., up to 50 rounds), indicating that sufficient warmup is beneficial.
federated scenarios. For the
Conclusion
present Federated Local Prior Alignment (FedLPA), novel framework for generalized category discovery in heterogeneous federated environments. Unlike prior approaches that rely unrealistic global knowledge or fixed class priors ill-suited for federated settings, Fedl.Po operates entirely at the client level. The framework first constructs a client-specific similarity graph, enhanced by

---

Table 4: Results with increased number of clients (N 10) standard benchmarks in non-i.i.d setting (a = 0.05). Methods with a dagger report results from [19]. The 'Server val.' column indicates whether server-side labeled validation data is used for evaluation. Bold black and plain red numbers indicate the best and second-best performance, respectively, in each column.
Server val.
CIFAR-I0 --
CIFAR-I00
ImageNet-
NatgeNee-OO00
Method
All
Old
New
All
Old
New
All
Old
New
FedAvg GCD! [22]
V
63.4
60.0
66.7
47.3
48.3
45.6
62.3
70.8
60.1
FodAvg GCL! [19]
68.2
64.2
70.1
52.5
53.9
51.0
67.3
74.5
60.8
FedAvg AGCLT [19]
63.8
70.3
52.2
53.6
52.4
67.5
74.8
61.1
FedLPA (ours)
92.3
94.5
91.2
53.6
53.1
71.7
85.6
64.7
FedLPA+ (ours)
93.7
96.2
92.3
55.9
59.7
54.0
72.1
86.8
64.7
67.5
67.3
68.5
68.9
68.5 69.1 69,9 70.1 71.6
67.5
68.9
70.1
68.6
69.8
0
65
ACC (%)
60
54.8
55.3
55.5
56.3 57.7
56.1
55.8
56.7
57.7
56.3
56.4
55
53.1
50.0 50.5
ACC
55
51.7
50
46.2
48.8
48.0
49.1
49.1
48.6
50
49.4
48.7
48,4
45
45
40
All Old New
All
Old New
40
10
30
40
50
70
80
90
0.1
0.3
0.5
(a) Identification percentile P(%)
(b) LPA regularization weight
70
70.1
68.4
68.1
69.8
68.2
70.1
70.8
72.1
71.9
0
0
65
ACC
55
50
60
$1.7
$7.7
50.4
56.2
55.6
49.5
55.4
48.5
48.2
54.8
48
63
48.950.0
54.3
57.7
51.7
59.2
53.7
60.5
54.8
55.6
45
40
Old
New
All Old New
33
0
2
4
6
8
20
40
(c) CLCD frequency R
(d) The number of warmup rounds
Figure 3: Ablative results of FedLPA hyperparameters in non-i.idd clients (a = 0.2) on Stanford- Cars. We examine the impact of: (a) the percentile P for known sample filtering in CLCD; (b) the weight for the LPA regularizer in Eq (3); (c) communication rounds between CLCD executions; and (d) the number of rounds for federated warmup training.
reliably pseudo-laheled known-class samples, to capture local data structures without requiring global information predefined category counts. Building this foundation, Local Prior Alignment (LPA) regularizer, integrated within a self-distillation scheme, dynamically adapts to local data distributions by aligning model predictions with an empirical class prior derived from these discovered structures. This synergy between local structure discovery and dynamic prior adaptation enables robust category discovery under severe data heterogeneity and class imbalance, yielding substantial performance gains over existing Fed-GCD methods across diverse benchmarks.
Acknowledgements
This work was partly supported by the National Research Foundation of Korea (NRF) grant [RS-2022- NR070855, Trustworthy Artificial Intelligence] and by the Institute of Information communications Technology Planning & Evaluation (IITP) grants -00-22222111220995 (No. No.2002-0-00955, (Part 2) Few-Shot Learning Causal Inference in Vision and Language for Decision Making, No.RS N0255- 25442338, AI star Fellowship Support Program(Scoul National University), No.RS-2021-113
10

---

Artificial Intelligence Graduate School Program (Seoul National University), 0...S-2021-1222266 Artificial Intelligence Innovation Hub] funded by the Korean government (MSIT).
References
[1] Durmus Alp Emre Acar, Yue Zhao, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. Federated learning based on dynamic regularization. In ICLR, 2021.
[2] Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas. Masked siamese networks for label-efficient learning. In ECCV, 2022. 3
[3] Kaidi Cao, Maria Brbic, and Jure Leskovec. Open-world semi-supervised learning. In ICIR, 2022. 1, 3
[4] Ting Chen, Simon Korblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations, In /CML, 2020.
[5] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hicrarchical image datahase. In CVPR, 2009.
[6] Yixin Fei, Zhongkai Zhao, Siwei Yang, and Bingehen Zhao. Xcon: Learning with experts for fine-grained category discovery. BMVC, 2022. 3
Kai Han, Andrea Vedaldi, and Andrew Zisserman. Leaminng to discover novel visual categories via deep transfer clustering. In CVPR, 2019.
Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. Mcasuring the effects of non- non-identical data distribu- tion for federated visual classification. arXiv preprint arXiv:1909.06335 2019. 7,7
[9] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian U Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for on-device federated learning. In ICML, 2020.
[10] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. In NewrlPS, 2020. 6
[11] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In /CCVW, 2013. 6
[12] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6
[13] Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi. Prototypical contrastive learning of unsupervised representations. In ICLR, 2021. 7,
[14] Haonan Lin, Wenbin An, Jiahao Wang. Yan Chen, Feng Tian, Mengmeng Wang, Qian Wang, Guang Dai. and Jingdong Wang. Flipped classroom: Aligning teacher attention with student in generalized category discovery. NeurlPS, 2024. 2.3
[15] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficeenn learning of deep networks from decentralized data. In AISTATS, 2017. 1, 3, 3,4,7
[16] Jona Otholt, Christoph Meinel, and Haojin Yang. Guided cluster aggregation: A hicrarchical approach to generalized category discovery. In WACV, 2024. 2,
[17] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In CVPR, 2012.
[18] Nan Pu, Zhun Zhong, and Nicu Sche. Dynamic conceptional contrastive learning for generalized category discovery. In CVPR, 2023. 2,
[19] Nan Pu, Wenjing Li, Xingyuan Ji, Yalan Qin, Nicu Sebe, and Zhun Zhong. Federated generalized category discovery. In CVPR, 2024. 2, 3, 7, 8, 10
[20] Sarah Rastegar, Mohammadreza Salchi, Yuki M Asano, Hazel Doughty, and Cees GM Snock. Sclex: Self-expertise in fine- tne-ggrined generalized category discovery. In ECCV, 2024. 2,
[21] Martin Rosvall and Carl T Bergstrom. Maps of random walks complex networks reveal community structure. Proceedings of the national academy of sciences, 105(4):1118 1123, 2008. 5
11

---

[22] Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman. Generalized category discovery. In CVPR, 2022. 1, 2, 3, 7,8, 10
[23] Sagar Vaze, Andrea Vedaldi, and Andrew Zisserman. Improving category discovery when representation rules them all. In NewrIPS, 2023. 1, 3
[24] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2.11.
[25] Hongjun Wang, Sagar Vaze, and Kai Han. SPTNet: An efficient altemative framework for generalized category discovery with spatial prompt tuning. In ICLR, 2024. 1,
[26] Ye Wang, Yaxiong Wang, Yujiao Wu, Bingchen Zhao, and Xueming Qian. Beyond known clusters: probe
new prototypes for efficient generalized class discovery. arXiv preprint arXi:2440.08995, 2024. 2
[27] Xin Wen, Bingehen Zhao, and Xiaojuan Qi. Parametric classification for generalized category discovery:
A baseline study. In ICCV, 2023. 2, 3, 8
[28] Jie Zhang, Xiansong Ma, Song Guo, and Wenchao Xu. Towards unbiased training in federated open-world semi-supervised learning. In ICMI., 2023. 2,
[29] Sheng Zhang, Salman Khan, Zhiqiang Shen, Muzammal Nascer, Guangyi Chen, and Fahad Khan. Prompt- cal: Contrastive affinity learning via auxiliary prompts for generalized novel category discovery. In CVPR, 2023. 1, 2
[30] Bingchen Zhao, Xin Wen, and Kai Han. Learning semi-supervised gaussian mixture models for generalized category discovery. CVPR, 2023. 2, 3. 7, 8
12

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly claim that FedLPA enables generalized category discovery in federated, non-IID settings; Sections 1, and 5 provide the algorithm and empirical evidence that substantiate this claim.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation long it is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
Justification: Section 6 ("Limitations & Future Work") articulates reliance on early-round feature quality, communication cost, and the need to study client churn and adversarial settings.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged
the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
13

---

Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [NA]
Justification: The paper contains no formal theorems or proofs; it contributes algorithm and empirical study.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification: Section 5 details datasets, Dirichlet partitioning, model architecture, training schedule, hyperparameters, and evaluation metrics, providing all information required to reproduce the main results.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to model. In general. releasing code and data often one good way to accomplish this, but reproducibilityy can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, other means that are appropriate to the research performed.
specific model and empirical evaluation, may
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily the architecture clearly and fully.
model architecture, the paper should describe
(c) the contribution is a new model (c.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open- open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibiliity In the case of closed-source models, it may be that access to the model is limited in
14

---

some way (e.g., to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
Justification: All datasets are public benchmarks. The authors will release anonymized code and detailed instructions on GitHub upon acceptance that anyone can reproduce the reported results.
Guidelines:
The answer NA means that paper does not include experiments requiring code. Please see the NeurIPS code and data submission guidelines (https://nips.. cc/
olicc) for more details.
While we encourage the release of code and data, we understand that this might be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https:
/ //nips. cc/publice /allii//guiesss sedeee/CooeSuuiiii ssionPolicy) for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: Datasets, split ratios, Dirichlet a values, optimizer (SGD), learning ratt schedule, temperature parameters, and other hyperparameters provided in Section (Experimental Setup).
Guidelines:
The answer NA means that the paper does include experiments.
The experimental setting should be presented the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: For each experiment, we report the average accuracy over multiple independent runs with different random seeds (3 runs). While we do not include error bars in plots or tables due to space limitations, the averaging procedure reduces the impact of variance and reflects stable results across runs.
15

---

Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results accompanied by enror bars, confi-
dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report I-sigma error bars, but one should state it. The authors should preferably report a 2 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are range (e.g. negative етгог rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed reproduce the experiments?
Question:
Answer: [Yes]
Justification: Section 5 states that experiments used NVIDIA RTX A6000/A5000 GPUs, batch 1 128, 50 communication rounds plus 20 warm-up rounds; this suffices to estimate compute cost.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make into the paper).
9. Code ethics
Question: Does the research conducted the paper conform, in every respect, with the NeurlPS Code of Ethics https: / /ww.uneursp...// httpppallltlhsasaiiiiiiiiaaaaeee
Answer: [Yes]
Justification: Only public datasets are used; no personal or sensitive data is processed, and experiments follow standard ethical practices.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there special consid- eration due to laws or regulations in their jurisdiction).
10. Broader impacts
16

---

Question:
Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: Section 6 highlights privacy benefits of FL and notes possible misuse risks in adversarial client scenarios.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate point that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: The work does not release high-risk models or scraped datasets; only standard public benchmarks are used.
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model. for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper. properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: All datasets (e.g., CIFAR, CUB-200) and models (ViT-B/16 DINO) are cited with original references and used under their respective open licenses.
17

---

Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package or dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g.. CC-BY 4.0) should be included for each asset. For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: No new dataset or pretrained model is released; only algorithmic code will be shared.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The study does not involve human participants crowdsourcing.
Guidelines:
The answer NA means that the paper does not involve crowdsoureing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurIPS Code of Bthies, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
18

---

Answer:
[NA]
Justification:
No human-subject research included, so IRB approval is not required.
Guidelines:
The answer NA means that the paper does involve crowdsourcing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
Justification: No large-language model is used in the core methodology; any LLM-assisted editing was purely for language polishing.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non non-stane non-standard components.
Please refer to our LLM policy (https / /ttp///erpp.//////e www.oo/eeeecncc//2222222220 for what should or should not be described.
19


Reviews:
[{"review_id": "jXx5gXEdaB", "reviewer": "Reviewer_mcj9", "summary": "- 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.", "questions": "My 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 $\\pi_{n, B_u}$.", "limitations": "The 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.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "After the review and rebuttal, the advantages of this work seem to outweigh the negatives. I believe that the paper meets the level of NeurIPS.", "rebuttal": "We appreciate the constructive feedback. We provide the following responses to address the questions.\n\n---------------------------\n\n**Q1. Novelty and broader import of FedLPA**\n\nWe thank the reviewer for the constructive feedback. We will address the core concern about the novelty and broader impact of our work.\n\n\nOur primary contribution is a novel Fed-GCD framework that **removes the unrealistic assumptions** prevalent in prior GCD works, both centralized and federated settings. Previous GCD methods, including federated adaptations, are built on two unrealistic assumptions in a real-world federated environment: \n\n\n- **Global Knowledge Assumption:** Most GCD methods like SimGCD [27], GCD [22], and even FedoSSL [28] require a priori knowledge of the total number of novel classes. In a federated system with strict privacy, no single client or server can possess this global information.\n\n- **Uniform Distribution Assumption**: Many approaches [16, 20, 23, 25, 27, 28, 30] enforce a uniform class distribution prior (e.g., via entropy maximization), which doesn’t make sense considering the highly skewed and heterogeneous nature of FL. \n\n\nWe propose a framework that effectively tackles the Fed-GCD problem by removing these assumptions. Specifically, our Confidence-guided Local Structure Discovery (CLSD) obviates the need for global knowledge by enabling each client to autonomously discover its unique local category counts and distributions. Building upon this, our Local Prior Alignment (LPA) then removes the uniform class distribution assumption by dynamically aligning the model's predictions with the actual local data distribution. CLSD and LPA mutually benefit each other, making our framework robust to severe data heterogeneity and fostering effective category discovery across clients.\n\nWe now address the reviewer's specific questions about LPA and CLSD.\n\n\n**Q1.1. Why does LPA compare logit and empirical distributions, and how does the LPA term relieve the challenges in FL+GCD?**\n\n**A1.1.** LPA achieves robust regularization by aligning the model's prediction distribution with an empirical prior—a \"softer\" approach that is more resilient to noise than using hard pseudo-labels. Since individual cluster assignments from clustering can be noisy, using these as hard targets for cross-entropy loss or contrastive loss would make the model highly sensitive to this noise, forcing overconfidence in incorrect labels. Instead, by matching the model's average prediction distribution on a batch to the empirical cluster distribution discovered by CLSD, LPA guides the model’s overall behavior to reflect the client’s class distribution. This makes the learning process inherently more stable and resilient to noise from the clustering step.\n\n\nThis directly resolves a major limitation of existing GCD works. Unlike previous methods that enforce uniform class distribution, LPA replaces this fixed, unrealistic assumption with a dynamic, data-driven regularization. It actively guides the model to learn each client's unique class distribution, thereby robustly handling the severe data heterogeneity and class imbalance that are unique challenges in Fed-GCD.\n\n\n**Q1.2. Technical novelty in local graph structure discovery and InfoMap**\n\n\n**A1.2.** While we utilize established tools like InfoMap, our technical novelty lies in their principled adaptation to solve a critical sub-problem in Fed-GCD: the assumption-free estimation of the unknown number and distribution of local categories at each client. Our proposed CLSD is a discovery engine whose primary function is to provide the dynamic, client-specific data distribution necessary for our LPA to work. This ability to perform robust local category discovery is a non-trivial prerequisite for the realistic Fed-GCD solution.\n\n\n\n**Q1.3. Any broader impacts and applications of this loss term design to other FL+GCD approaches?**\n\n\n**A1.3.** LPA can be extended beyond our specific framework, as its core principle offers a principled and robust alternative to the unrealistic uniform prior assumption. We believe LPA can be readily adapted as a \"drop-in\" module in existing parametric GCD methods (e.g., SimGCD), extending their applicability to the realistic, imbalanced data distributions where they struggle, in both centralized and federated settings.\n\n-------------------------------\n\n**Q2. 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, or via empirical results?**\n\n\n**A2.** Thank you for your constructive feedback. While a formal theoretical convergence proof is challenging due to the non-differentiable nature of the InfoMap, we provide empirical evidence demonstrating the stable and efficient convergence of our method. Across all our experiments, the model consistently converges within the reported communication rounds (20 warm-up, 50 training). This demonstrates faster convergence compared to baselines such as AGCL, which require up to 200 rounds for training. To provide a picture of convergence, we conducted an extended training run of 100 rounds (including warmup rounds). The results are presented in Table A below. As shown, both on CIFAR-100 and Stanford Cars, the performance converges around 60 rounds and maintains it thereafter. We will include the full convergence curves for all experiments in Tables 1 and 2 in the final manuscript.\n\n\n**Table A: Convergence of FedLPA on Stanford Cars and CIFAR-100 ($\\alpha=0.2$). The table shows the \"All\" accuracy (%) over communication rounds.**\n\n|        Rounds      | 0    | 10    | 20   | 30    | 40   | 50   | 60   | 70   | 80    | 90   | 100  |\n| :----------- | :--: | :---: | :--: | :---: | :--: | :--: | :--: | :--: | :---: | :--: | :--: |\n| **Stanford-Cars** | 17.8 | 29.2 | 31.7 | 39.9 | 49.3 | 51.9 | 52.4 | 52.1 | 53.9 | 53.3 | 53.2|\n| **CIFAR-100** | 38.7 | 42.3 | 45.8 | 55.1 | 55.4 | 56.3 | 57.1 | 57.7 | 57.6 | 57.2 | 57.3|\n\n-------------------------------\n\n**Q3. How to compute the empirical prior?**\n\n\n**A3.** The empirical prior $\\pi_{n, B_{u}}$ is not computed from ground-truth labels, but is instead empirically derived from the pseudo-labels generated by our framework. Specifically, at the start of each local training phase, our CLSD module and InfoMap (Sec. 3.3-3.4) assigns a cluster ID (a pseudo-label) to every local sample. Following Eq. 4 in the main paper, the prior $\\pi_{n, B_{u}}$ is then simply the normalized histogram of these pre-assigned pseudo-labels for the samples within the current unlabeled mini-batch $B_{u}$.\n\n\nWe hope our responses have addressed your concerns. We welcome any further questions."}, {"review_id": "N4Q6U3qFs1", "reviewer": "Reviewer_3rHf", "summary": "This 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.", "questions": "1. 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.", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I confirm this review.", "rebuttal": "We appreciate the constructive and insightful feedback. We address your questions below and welcome any further questions.\n\n\n**Q1. 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. Could global novel-class prototypes be gradually learned via communication of aligned clusters or prototypes?**\n\n\n**A1.** We thank the reviewer for this insightful comment. FedLPA tackles the misalignment problem via relational modeling. Specifically, seen-class representations act as globally consistent anchors, learned with shared semantics across the federation. By modeling each client's novel classes in relation to these common anchors (via local relational graph construction), our approach can establish a consistent relational structure without requiring direct semantic alignment of the novel classes themselves. This, in turn, enables consistent representation learning for all classes by focusing on their relationships to the shared anchors.\n\nOur ablation study (Row 5 in Table 3) validates this anchor-based mechanism. The graph refinement establishes a clearer relational structure using seen-class examples, resulting in performance improvement. \n\n\n**Q2. 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\n**A2.** We thank the reviewer for the constructive comments. To analyze the sensitivity of FedLPA to the quality of local clustering, we conduct an experiment on Stanford-Cars ($\\alpha=0.2$) by intentionally injecting noise into the pseudo-labels generated by our Confidence-guided Local Structure Discovery (CLSD) module. At the beginning of every CLSD phase (every $R=5$ rounds), we randomly flipped a certain percentage of the pseudo-labels to other classes.\n\n\nThe results, presented in Table B, show that while performance degrades gracefully as the label noise rate increases, FedLPA maintains robust performance. Even with a 30% noise rate, the accuracy remains competitive and outperforms the baselines reported in our main paper (Table 1). This demonstrates that our framework is not overly sensitive to a moderate level of noise in local clustering and can effectively mitigate its negative impact.\n\n\n**Table B: Robustness of FedLPA to noisy pseudo-labels on Stanford-Cars ($\\alpha=0.2$). We report \"All\" accuracy (%) under varying rates of injected label noise.**\n\n| Pseudo-label Noise Rate | All | Old | New |\n |------------|------|------|------| \n| 0 % |  52.1 | 67.6 | 44.6 | \n| 10 % |  48.3 | 60.1 | 42.6 |\n |20 % |  47.7 | 63.0 | 40.3 | \n| 30 % |  47.2 | 59.7 | 41.1 |\n\n\n**Q3. How does FedLPA compare to pure federated contrastive learning methods?**\n\n\n**A3.** Direct comparison with the other federated contrastive learning methods, such as MOON [Li21], FedRCL [Seo24], and FedMoco [Dong21], is not straightforward since our work focuses on discovering novel classes in unlabeled data and learning proper representations along with seen classes, while these methods are not inherently designed to discover or model the underlying semantic category structures, which is the central challenge in Fed-GCD and the primary focus of FedLPA.\n\n\n**References**\n\n[Li21] Q. Li, et al., Model-Contrastive Federated Learning, in CVPR, 2021\n\n[Seo24] S. Seo, et al., Relaxed Contrastive Learning for Federated Learning, in CVPR, 2024\n\n[Dong21] N. Dong, et al., Federated Contrastive Learning for Decentralized Unlabeled Medical Images, in MICCAI, 2021"}, {"review_id": "MMoJrs6nAX", "reviewer": "Reviewer_Q1AF", "summary": "This 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.", "questions": "pls refer to the weakness part.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths.\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.", "was_revised": true, "final_justification": "My concerns have almost been addressed, so i will keep my positive score.", "rebuttal": "We sincerely thank the reviewer for the thoughtful feedback and for acknowledging the key strengths of our work. We are grateful for your positive comments on our model being technically sound, well-motivated, and addressing a critical research problem. We address your questions below.\n\n\n**Q1. The 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\n\n**A1.** We thank the reviewer for this practical concern. While the local structure discovery (CLSD) phase indeed introduces additional computational cost, we believe that the resulting performance gains significantly outweigh this overhead. Crucially, this cost can also be flexibly managed.\n\nTo quantify the cost, our experiments on fine-grained datasets (when $R=1$ with a ViT-B/16 backbone on a single A6000 GPU) show the CLSD process takes approximately 7.2 seconds per client, constituting about 11.4% of a 63.12-second wall-clock time for a local training round. \n\nMore importantly, this overhead is not a fixed bottleneck. As shown in Figure 2c in the main paper, practitioners can directly control this cost by adjusting the CLSD frequency, $R$. The framework maintains highly robust accuracy even when CLSD is performed less frequently. For instance, by setting $R=8$, the CLSD component is executed only once every eight rounds. This reduces its overall contribution to the total computational cost from 11.4% to just 1.42%, while still maintaining competitive performance. This flexibility offers a practical way to adapt our method to various resource-constrained environments.\n\n\n**Q2. 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\n\n**A2.** Thank you for the excellent question. You are correct; we chose Jensen-Shannon Divergence (JSD) for our LPA regularizer precisely because its symmetric and bounded properties led to empirically stable training.\nWe did experiment with the standard KL divergence, but we found the training to be unstable. The loss often became biased towards high-probability classes or would explode due to near-zero probabilities in the model's predictions. Even with heuristic adjustments such as adding an epsilon value or clipping the distribution, the instability persisted. In contrast, JSD provided a consistently robust and reliable training signal without such modifications.\n\n\n**Q3. How sensitive is this method to the quality of warm-up?**\n\n\n**A3.** We have analyzed the sensitivity to the warm-up quality through an ablation study on the number of warm-up rounds, and the results are illustrated in Fig. 4a in the supplementary document. The results show that while performance improves with more warm-up rounds, our method is not overly sensitive to it, which demonstrates the robustness and efficiency of our approach."}, {"review_id": "IMaGyRo6hP", "reviewer": "Reviewer_QDuW", "summary": "The 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\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.", "was_revised": true, "final_justification": "The authors addressed most of my concerns except the theoretical justification. I raise my score.", "rebuttal": "We truly appreciate your positive and constructive comments, and we will reflect on your feedback thoroughly. Here are the responses to the questions.\n\n**Q1. Can the authors provide any theoretical or empirical analysis on the convergence behavior?**\n\n**A1.** Thank you for your constructive feedback. While a formal theoretical convergence proof is challenging due to the non-differentiable nature of the InfoMap, we provide empirical evidence demonstrating the stable and efficient convergence of our method. Across all our experiments, the model consistently converges within the reported communication rounds (20 warm-up, 50 training). Note that FedLPA shows faster convergence compared to baselines like AGCL, which require up to 200 rounds for training. To provide a picture of convergence, we conducted an extended training run of 100 rounds (including warmup rounds). The results are presented in Table C below. As shown, both on CIFAR-100 and Stanford Cars, the performance converges around 60 rounds and maintains it thereafter. We will include the full convergence curves for all experiments in Tables 1 and 2 in the final manuscript.\n\n**Table C: Convergence of FedLPA on Stanford Cars and CIFAR-100 ($\\alpha=0.2$). The table shows the \"All\" accuracy (%) over communication rounds.**\n|        Rounds      | 0    | 10    | 20   | 30    | 40   | 50   | 60   | 70   | 80    | 90   | 100  |\n| :----------- | :--: | :---: | :--: | :---: | :--: | :--: | :--: | :--: | :---: | :--: | :--: |\n| **Stanford-Cars** | 17.8 | 29.2 | 31.7 | 39.9 | 49.3 | 51.9 | 52.4 | 52.1 | 53.9 | 53.3 | 53.2|\n| **CIFAR-100** | 38.7 | 42.3 | 45.8 | 55.1 | 55.4 | 56.3 | 57.1 | 57.7 | 57.6 | 57.2 | 57.3|\n\n\n\n\n\n\n**Q2. Have you observed cases where the learned cluster structures diverge across rounds?**\n\n**A2.**  In our experiments, we found the learned cluster structures to be stably evolved and did not observe divergence during the learning process. We attribute this stability largely to the design of our CLSD mechanism. By establishing stable anchors within the similarity graph—using both ground-truth labels and high-confidence pseudo-labels from the seen classes—CLSD builds a reliable foundation for the graph. We believe this robust foundation mitigates drastic fluctuations in the overall cluster structure between rounds, providing a consistent learning target for the model.\n\n**Q3. Communication efficiency of FedLPA**\n\n**A3.** FedLPA is communication-efficient because it requires fewer total communication rounds to converge without incurring additional per-round costs compared to FedAvg.\n\nSince FedLPA only communicates model parameters, its per-round cost is identical to standard FedAvg and lower than methods like AGCL [19], which communicate extra information such as class prototypes. Furthermore, we empirically observed that FedLPA consistently converges within approximately 70 total rounds (20 warm-up and 50 training) across all datasets. This rapid convergence results in higher overall communication efficiency, as FedLPA reaches its final performance in fewer rounds than baselines requiring more extensive communication rounds (200 rounds reported in [19]).\n\n\n**Q4. Could any of the learned local graph structures or soft predictions leak sensitive information?**\n\n**A4.** No, FedLPA does not introduce additional privacy risks compared to the standard FedAvg protocol since FedLPA only communicates model parameters. Unlike methods such as AGCL that communicate class prototypes and risk leaking information about local class distributions, in FedLPA, the learned local graph structures, discovered concepts, and predictions are strictly confined to each client and never shared with the server or other clients. Therefore, FedLPA maintains the same privacy guarantees as FedAvg.\n\n\n**Q5. Have you considered adding differential privacy or gradient compression techniques?**\n\n**A5.** Thank you for the great suggestion. These techniques are orthogonal to our core method and can be readily integrated to enhance privacy and efficiency. We view this as a valuable direction for future work.\n\n**Q6. Do you have quantitative or qualitative results showing how graph quality evolves across rounds?**\n\n**A6.** We thank the reviewer for this constructive suggestion. We provide a quantitative analysis below, which shows that FedLPA consistently improves the quality of the graph-based local structures as training progresses.\nTable D presents the clustering accuracy on each client's local unlabeled data, which directly reflects the graph quality. The average accuracy steadily increases from 57.0% to 71.3%, demonstrating a positive feedback loop: better representations lead to higher-quality graphs, which in turn improve the model. This confirms that our framework reliably refines its understanding of local data structures throughout training.\n\n**Table D: Per-Client Clustering Accuracy (%) Progression of FedLPA on Stanford Cars ($\\alpha = 0.2$)**\n\n| Rounds | 20 | 30 | 40 | 50 | 60 | 70 |\n| :------- | :------: | :------: | :------: | :------: | :------: | :------: |\n| Client 0 |   58.6   |   66.9   |   71.3   |   72.5   |   73.3   |   73.3   |\n| Client 1 |   55.6   |   62.4   |   65.5   |   67.4   |   67.9   |   69.2   |\n| Client 2 |   58.5   |   65.7   |   69.4   |   70.2   |   71.2   |   70.8   |\n| Client 3 |   57.8   |   69.2   |   72.1   |   73.1   |   74.4   |   74.0   |\n| Client 4 |   54.6   |   65.8   |   68.5   |   68.5   |   68.9   |   69.2   |\n| **Avg**  | **57.0** | **66.0** | **69.4** | **70.3** | **71.1** | **71.3** |\n\n\n**Q7. Have you considered alternatives like initializing from a pre-trained encoder (other than DINO), or delaying the clustering process until later rounds?**\n\n**A7.** Our framework is indeed flexible regarding the choice of the backbone; we used DINO primarily for a fair comparison with baselines.  To further validate your suggestion, we ran additional experiments on Stanford-Cars using a stronger DINOv2 backbone. The results show that FedLPA achieves a significant performance gain (e.g., ACC increased from 52.1% to 62.4% for $\\alpha=0.2$ and from 51.8% to 58.1% for $\\alpha=0.05$ ), highlighting that FedLPA directly benefits from the better backbone. We will include this result in the final manuscript. Thank you for this valuable suggestion.\n\nRegarding delaying the clustering, we have already explored this through our ablation study on the number of warm-up rounds (Fig. 4a in the supplementary document). As you suggested, the results show that performance improves with more warm-up rounds. Notably, our method achieves strong performance that already outperforms most baselines with as few as 10 warm-up rounds, demonstrating the robustness and efficiency of our approach.\n\n**Q8. The title includes \"Local Prior Alignment,\" which sounds Bayesian, but no formal Bayesian formulation or interpretation is provided.**\n\n**A8.** To clarify, our method is not Bayesian. We use \"Local Prior\" to denote the empirical cluster distribution estimated from local data, which serves as a dynamic target for alignment, distinct from a Bayesian prior probability."}]

Author rebuttal:

Meta-review: The paper addresses the Generalized Category Discovery (GCD) task in federated settings. It is a new extension of federated learning. The proposed solution is reasonable with limited scientific contribution.

**strengths**

This work advances federated learning research by exploring a new and practical scenario.

The proposed solution is technically sound and well-supported by empirical analysis.

**weaknesses**

The design choices should be further discussed and better justified.

Given the composite nature of the proposed solution, a convergence analysis would strengthen the work by providing stronger theoretical guarantees.

**recommendation**

I recommend accepting this paper as an innovative contribution to federated GCD. It is expected that the authors will continue to refine this direction in future work, potentially providing a method with stronger theoretical convergence guarantees.

**reviewer consensus**

All reviewers gave positive scores for this work, with some reviewers increasing their scores after the rebuttal.
Decision: Accept (poster)