Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
Authors: Guoqing Chao
Zhenghao Zhang
Lei Meng
Jie Wen
Dianhui Chu
Venue: ICML 2025 poster (2025)
Keywords: Multi-View Clustering
Graph Convolutional Networks
Federated Learning
Primary area: general_machine_learning->clustering

Abstract: Federated multi-view clustering has been proposed to mine the valuable information within multi-view data distributed across different devices and has achieved impressive results while preserving the privacy. Despite great progress,  most federated multi-view clustering methods only used global pseudo-labels to guide the downstream clustering process and failed to exploit the global information when extracting features. In addition, missing data problem in federated multi-view clustering task is less explored. To address these problems, we propose a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG). Specifically, we designed a dual-head graph convolutional encoder at each client to extract two kinds of underlying features containing global and view-specific information. Subsequently, under the guidance of the fused graph, the two underlying features are fused into high-level features, based on which clustering is conducted under the supervision of pseudo-labeling. Finally, the high-level features are uploaded to the server to refine the graph fusion and pseudo-labeling computation. Extensive experimental results demonstrate the effectiveness and superiority of FIMCFG.  Our code is
publicly available at https://github.com/PaddiHunter/FIMCFG.

Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
Guoqing Chao Zhenghao Zhang'
Lei Meng?
Jie Wen
Dianhui Chu
Abstract
ing in multi-view data (Chao al., 2025a). Multi-view clustering (MVC) is popular machine learning paradigm designed to group data using complementary and consis- tent information from multiple views. In recent years, deep learning has been widely used in MVC, known as deep multi-viww clustering, which has achieved state-of-the-art clustering performance due to its excellent representation learning ability (Chen al., 2022; Yan et al., 2021; Yu et al., 2024). In practice, due to the complexity of the data collection and transmission process, data may be missing for some views, leading to the Incomplete Multi-view Problem (IMP) (Lin et al., 2021). To address IMP, some incomplete multi-view clustering methods adopt data recovery methods to predict missing data. Recently, graph neural networks have received much attention for their ability capture structural information. In incomplete multi-view clustering field, graph convolutional neural networks (GCNs) can com- pute missing data features with the help of graph structural information and neighboring node attributes, reducing the impact of missing data (Chao et al., 2024).
Federated multi-view clustering has been pro- posed to mine the valuable information within multi-view data distributed across different de- vices and has achieved impressive results while preserving the privacy. Despite great progress, most federated multi-view clustering methods only used global pseudo-labels to guide the down- stream clustering process and failed to exploit the global information when extracting features. In addition, missing data problem in federated multi-view clustering task is less explored. To address these problems, we propose novel Fed- erated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG). Specifically, we designed a dual-head graph con- volutional encoder at each client extract two kinds of underlying features containing global and view-specific information. Subsequently, un- der the guidance of the fused graph, the two un- derlying features are fused into high-level fea- tures, based on which clustering is conducted under the supervision of pseudo-labeling. Fi- nally, the high-level features are uploaded the server refine the graph fusion and pseudo- labeling computation. Extensive experimental results demonstrate the effectiveness and superi- ority of FIMCFG. Our code is publicly available at https//ggttbb..oo///aaadHHe/e/ee
Federated learning is distributed machine learning paradigm that allows multiple clients to jointly train en- semble model without exposing raw data information (Liu et al., 2020; Qi et al., 2025b). It aims for dual optimiza- tion objectives of global generalization and local personal- izanion(Meng et al., 2024), and focuses on addressing the challenges posed by heterogeneous data distributions and missing classes(Qi et al., 2023; 2025a). Federated multi- view learning designed to learn a global model from multi-view data distributed across different clients. The main challenges are formulated from privacy preservation and the heterogeneity of multi-view data. In addition, multi- view data distributed on different clients is often incomplete and some views may be missing, exacerbating the complex- ity (Chen et al., 2023).
1. Introduction
With the fast development of information collection tech- niques, data can be obtained from different views, result-
'School of Computer Science and Technology, Harbin Insti- tute of Technology, Weihai, China "chooo of Software, Shandong University, Jinan, China 'Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technol- ogy, Shenzhen, China. Correspondence to: Guoqing Chao <guo- qingchao @ edt.e.
There exist some research works combining graph neural networks (GNN) with federated multi-view clustering (Yan et al., 2024). However, most of current GNN-based fed- erated multi-view learning only used the graph structure information accompanied by noise and didn't 't tackle the missing data problem. In addition, in most federated multi- view clustering models, the client tends extract features for single view and on the server, the features fused
Proceedings of the 4nnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Federated Incompletc Multi-view Clustering with Globally Fused Graph Guidance
to compute pseudo-labels acting as global self-supervised information to guide the client training. In summary, most of these approaches only mine the global information for downstream clustering. while ignoring the interaction of multiple views in upstream feature extraction, making it difficult to capture the global information within multiple views, degrading the clustering performance.
tion, transmission, or storage factors. Incomplete Multi- View Clustering (IMVC) addresses this challenge by learn- ing the robust clustering structures from partially observed multi-view data. Recent advances in deep learning have significantly enhanced IMVC performance, with various innovative approaches proposed. For instance, Completer (Lin et al., 2021) leverages the autoencoders to maximize the cross-view mutual information via contrastive learn- ing, ensuring view-consistent representations. Addition- ally, it employs the dual prediction to minimize the con- ditional entropy, effectively recovering the missing views. Another approach, based on variational autoencoders(Xu et al., 2024), utilizes the Product-of -f---eertsssssssseees (PoE) method to aggregate multi-view information, deriving a shared latent representation to handle incompleteness. Further improv- ing data recovery, AIMC(Xu et al., 2019) integrates the element-wise reconstruction with Generative Adversarial Networks (GANs) to generate the plausible missing data. More recently, Graph Neural Networks (GNNs) have been introduced to IMVC, capitalizing on their ability to model relational data. For example, ICMVC(Chhao et al., 2024) tackles the missing data through multi-view consistency rela- tion transfer combined with Graph Convolutional Networks (GCNs). Similarly, CRTC(Wang et al., 2022) introduces cross-view relation transfer completion module, where GNNs infer missing data based on transferred relational graphs.
To solve the above problems, we propose the Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG). Clients include upstream two-level feature extraction and downstream clustering. To address incomplete data problem, the global graph structure migration is proposed, which repairs the incomplete local graph. With the information propagation mechanism of GCN, the latent feature of missing data can be learned from the neighboring nodes in repaired graph. With the guidance of globally fused graph, two-level feature extraction has three parts: dual-head graph encoder, decoder and fusion module. We input the fused graph from the server and the repaired local graph into the
graph encoder to ex- tract underlying features including global and view specific information. Due rich view specific information included, underlying features are used by decoder reconstruct data and a fusion module is introduced to fuse the underlying features into a low-dimensional high-level feature under the guidance of globally fused graph. With the fused graph, global information is propagated in feature extraction. Then, we perform clustering on the high-level features and op- timize the clustering layer with the global supervision of pseudo-label. At the server, we perform graph fusion, fea- ture fusion and global clustering to obtain the fused graphs and pseudo-labels, which are used as global information to guide the client. The main contributions of our work are summarized as follows:
2.2. GNN based Multi- Clustering
In recent years, GNNs have been widely used in multi-vien view clustering due to their powerful feature extraction ability to exploit the graph structure and node attribute informa- tion, and have received more and more attentions (Xia et al., 2022: Du et al., 2023). GNNs have also been studied to transfer the inter-view graph structure to deal with the miss- ing data problem. For instance, Chao et al. (2024) proposed to use multi-view consistency relation migration and GCN to tackle the missing data problem in multi-view clustering task. In addition, several studics have found that propa- gating information within the single view could limit the performance. To solve this problem. Xiao et al. (2023) proposed a dual fusion module and a dual information prop- agation mechanism to capture multiple information of dif- ferent views. Wang et al. (2024b) learned the consensus representations through the unified heterogeneous attribute graphs, which can propagate structural information across multiple views to improve the feature representation. Chao et al. (2025b) proposed hierarchical citemattion-ranssfere incomplete multi-view clustering method that integrates view-specifie representation learning, global graph propaga- tion, and contrastive clustering.
A federated incomplete multi-view clustering frame- work with dual-head GCN encoders is proposed. By introducing the globally fused graph guidance, the en- coders on clients are able to to grasp the global infor- mation among views.
The global graph structure migration is proposed to repair incomplete local graphs, which is used to esti- mate latent features of missing data. It improves the accuracy of the estimated features of missing data, Ex- tensive experiments on real-world multi-view data sets demonstrate the superior performance of our proposed model.
2. Related Works
2.1. Incomplete Multi-View Clustering
In real-world applications, multi-view data often suffer from missing data problems due to uncontrollable data collec-
However, the aforementioned GNN-based MVC methods can only address the missing data problem and informa-
2

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Federated Incompletc Multi-view Clustering with Globally Fused Graph Guidance
tion propagation limitations in centralized environments. Although some federated learning multi-view clustering methods have been proposed for distributed scenarios, their clients often do not explicitly consider the global informa- tion in the upstream feature extraction process. In addition, there are fewer studies on how to exploit the multi-view clustering capability of GNNs distributed environments. In this paper. we propose a novel GCN-based federated multi-view clustering (FedMVC) approach, which solves the missing data problem and restricted global information propagation in distributed scenarios.
K clusters. Each client has only one view-specific data XT = {x.' - :-..:, 2N } TRN=x where D, de- notes the dimensionality of the samples in view Due to incomplete samples in the clients, Nm S N. In order to facilitate the processing, for those missing samples, we use the zero vector O to fill in, i.e. X E NR The graph structure of the raw data is represented by adjacency matrices A = { A2, "...,".."""
and A E {0, 1|NxN AT 1 0 denotes the presence or absence of edges between s!" and xy" The adjacency matrix of the fused graph from the server is denoted as A E (0.1)NXN.
A" J.
Our model architecture consists of M clients and one server. Each client utilizes its private data for local view training. The high-level features {/IT J==1 are obtained through two level feature extraction guided by the globally fused graph A. Then clustering is performed based on them. Based on the clustering results, we compute the weights {wiT) MMM m== by
2.3. Federated Multi-View Clustering
Federated Multi-View Clustering is emerging task which aims to conduct clustering task for multi-view data through collaboration of clients. The heterogeneity of multi-view data and the importance of privacy preservation make it ex- tremely difficult to handle. Chen et al. (2023) proposed sam- ple alignment and data extension techniques to explore the complementary cluster structures of multiple views. Based on Chen et al. (2023), Ren et al. (2024) utilized sample commonality and view generality to adaptively generate alignment matrices to further address data misalignment across view clients. To deal with the heterogeneous hybrid views problem, Chen et al. (2024) designed a local collab- orative contrastive learning approach to address the client gaps and global specific weighted aggregation method to reduce view gaps. Yan et al. (2024) adopted the heteroge- neous GNN encoders to address the data heterogeneity at clients and designed a global pseudo-labeling mechanism with heterogeneous aggregation in a federated environment to deal with the incomplete view problem.
high-level feature. After that, the server receives {H ) mm and {w"})MM 1==1 from the clients and performs feature fusion, graph fusion and global clustering. The overview illustration of the model is shown in Figure 1.
3.2. Client Local Training with Global Guidance
As shown in Figure 1, The client contains two steps with feature extraction and clustering. client M extracts the un- derlying features M and high-level features HM under the guidance of fused graph A, such that they can capture the global information of multiple views. Through the initializa- tion of the global clustering center UM and the supervised training with pseudo- labels P. the clustering layers of dif- ferent clients are aligned and obtain consistent clustering structure. Subsequently, we take client m an example to introduce the local training of clients.
Although federated multi-view clustering works have made great progress, they tend to extract the features at the client first, and then aggregate the features at the server and compute the pseudo-labels to transfer to the client as self- supervised information. Pseudo-labeling often only guides the downstream clustering process and is not useful for the upstream feature extraction process, which limits the fea- ture extraction process from utilizing global information. To solve this problem, we propose FIMCFG to improve the feature representation of clients by effectively using the fused graph from the server, such that the dual-head graph encoder grasps the global information of multiple views.
1.2.1. GLOBAL GRAPH STRUCTURE MIGRATION
In the beginning, because there is no graph structure in- formation on client m, the local graph adjacency matrix ATh E f0. 1) NxN needs to be constructed from the raw data. We use the radial basis function compute the similar- ity matrix STm E [0, N which is calculated as follows:
where S E [0, 1] denotes the similarity between x." and a.. After that, we set the largest elements of each row to and the others to 0 in S to construct the adjacency matrix A where or 0 denotes the presence or absence of edges.
3. Proposed Method
3.1. Problem Formulation
We give a formal definition of federated multi-view clus- tering. Suppose there is dataset containing N sam- ples with M views distributed over M clients (denoted as X = x x2,..,,"""""....""" MM). }), which will be divided into
To handle the incomplete data problem, global graph struc- ture migration is proposed. Under the GCN encoder on clients, each sample estimates single-view features accord- ing to its attribute and neighboring nodes" attributes. Since
3

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Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
Feature Extraction
Clustering
Client
Deakked
61
(
Init
Feature Fusion
Graph Fusion
x!
W
Tay (
10,
Fuiden wodele
w
A 4 of
Consstenacchi..)
B,
AM
Feature Extraction
Clustering
IEM,
Global Clusering
Denoder
broadcast
19
444
N OO AM)
g
init UM
P.UMA
average
10
XM
QM
7
M
Fanion
(P101 RP Separate((!))
15 4 1 12" 8" Concatenatt (22)
Client M
Server
Figure 1. Overview of the FIMCFG framework. It contains M clients and server. (1) Client: M-th client consists of two processes: feature extraction and clustering. With the guidance of fused graph A, client extracts underlying features H1 by dual-head graph encoder and high-level features HM by fusion module. Under the supervision of pscudo-labels P, the clustering centers RM are optimized by reducing the KI. loss between soft distribution Q" a and P. Based on clustered H M the silhouette coefficients w are calculated as aggregation weights, (2) Server: the server performs feature fusson with [HT)) 1m=1 obtain global features H and graph fusion with latent graph { jm= of [[ centers and pseudo labels P.
j to obtain fused graph A. Then the server perform K K-means algorithm on H to get global clustering
missing samples are represented by zero vectors, the encoder automatically ignores them during computation. However, when using similarity-based calculations for the local adja- cency matrix, missing samples represented as zero vectors are isolated from complete samples. making their feature computation infeasible. To address this issue, we propose global graph structure migration technique, which replaces rows corresponding to missing samples in the local adja- cency matrix with corresponding rows from the global adja- cency matrix. This approach enriches the local adjacency matrix with global structural information, enabling the miss- ing samples to estimate features based on adjacent complete samples.
local graph At and fused graph are input to the dual- head graph encoder to extract two different underlying fea- tures H and Hy" Specifically, stacked GCN layers are used encoders and the global graph encoder föy and local graph encoder för. are used as two heads. Their map-
ping functions are if (X A) - HT Nxdm and for (Xmh, AAT)) - H E R N xxdm, where HT and H. are the underlying features containing global and local view information, respectively. The computation process of the I-th layer of the local graph encoder is denoted as:
H. (0) (D - AT - HH (t-1) W + bb))
(2)
+ -H (t-1),
3.2.2. TWO-LEVEL FEATURE EXTRACTION WITH FUSED GRAPH GUIDANCE
where (-)) is activation function. Am = T + I and D E, Aty. is the unit matrix, and W and 6%) are the trainable parameters of the t-th layer. A is the fixed local graph after global graph structure migration. Skip connections are introduced to prevent model degradation. The global graph encoder is computed in the same way with local graph encoder.
As demonstrated in Figure 1, the client's feature extraction consists of three parts: the dual-head graph encoder, the decoder and the fusion module. Guided by the globally fused graph, they are jointly trained to extract two-level features.
Dual-Head Graph Encoder. Dual-head Graph Encoder contains two parallel graph encoders. The samples X
The dual-head graph encoder is identified by optimizing
4

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Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
the graph reconstruction loss such that features retain graph structure information. The latent graphs Ag E [0, 1] N and ATT E [0, 1|/x are constructed by underlying features Hn and H1T according Eq. (1). Their elements indicates the strength of the connection, 0 means no connection and means strong connection. The graph reconstruction loss of the underlying features is calculated as follows:
3.2.3. PSEUDO-LABEL-CUIDEE CLUSTERING TRAINING
To obtain the clustering assignment, we construct a cluster- ing layer agm with trainable parameters {uz E R } y=1 where K represents the number of clustering and u rep- resents the clustering center of the j-th cluster on client m. The soft distribution QT E RN is computed as follows:
L = A
21
|
m - A 12
(3)
(1 1 -- 11
-07"11 9. ΣØ= (1 + - -3
(7)
L
D
where 9j denotes the probability that the i-th sample is assigned the j-th cluster, and hTT Rd denotes the high-level feature of the i-th sample. At the beginning of each training round, the client initializes the clustering layer with the global clustering center U to align the classes. We use pseudo-labels from the server to supervise the training of the clustering layer such that different clients can obtain a consistent clustering structure. The KI. divergence loss between the soft distribution QTT and the pseudo-labels P is used to optimize:
Decoder. In order enhance the robustness of the model, set up decoder with fully connected layer for the un- derlying features. The mapping function is 9 Hm E R N x2 - Xm E RNDx... where 1 represents the underlying feature matrix concatenated by Hm and Hi" and Xm denotes the reconstructed samples. The content reconstruction loss used to optimize:
1 1 1
(4)
Due to the rich view information, underlying features are suitable for reconstructing samples. Note that we compute the content reconstruction loss only for complete samples and use the predicted reconstructed samples to replace the missing samples at the end of training round.
L. (RI("))
ΣΣ ==1==1
Pty log
Pij 4
(8)
In addition, when not yet communicating, the client has no available pseudo-labels since the server has not yet per- formed aggregation. Therefore, during the first round of training, the client supervises the training of the clustering layer using the target distribution pm E N instead of P. pm is computed by the following equation:
Fusion Module. Since the two underlying features may contain conflicting components and redundant information which are not suitable for clustering, we introduce fusion module that fuses the two underlying features and maps them into low- di-edmiiiiinnlll high-level feature space. The fusion module uses fully connected layers with a mapping function: før. Hm Nx2d.. - H RNxd,, where nT is high-level feature. In order to learn a consis- tent representation, globally fused graph is used guide the training of fusion module. The consistent graph reconstruc- tion loss for high-level features is computed as follows:
(4)) E 9:3 )2 P = E, >, )2)
Therefore, the optimization objective of client m consists of three parts:
1m= L + + + L2
(10)
LT = I|ÁT - 2
(5)
where 71 and 72 are the trade-off parameters to balance the clustering loss, graph reconstruction loss and content reconstruction loss.
where ÅM E N is the latent graph constructed from high-level features H by Eq. (1). The fusion module each client aligns the fused high-level feature graph closer to the global graph, while the global graph is collaboratively updated by all clients. Through iterative updates, the model converges to a stable state, ultimately resolving conflicting components. Up to this point, we merge the graph recon- struction loss of dual-head graph encoder and fusion module as:
3.2.4. AGGREGATION WEIGHT
Silhouette comprehensively evaluates clustering quality by considering both intra-cluster cohesion and inter-cluster separation (Rousseeuw, 1987). It ranges between |-1, 1]. where values close to indicate that samples are effectively clustered and well-separated from other clusters, reflecting strong clustering performance. Conversely, values close to -1 suggest incorrect cluster assignments and poor clus- tering quality. Thus we use it as the weights for server
I = L
(6)
During the communication phase, the client uploads high- level features to the server, which protects data privacy.
5

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Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
aggregation. At the end of client's training, the client com- putes the silhouettes n R of the high-level feature and sends them to the server. The silhouettes for the i-th sample are calculated as follows:
Feature Fusion. The feature aggregation is conducted as follows:
W H T 11, w M' H
(14)
b(1) a(1)
where H E RR dm denotes the global feature, and I..... - stands for matrix concatenation operation. w"
(11)
max maxfa(i),b(M)
+ log(1 +
Σ Im=l/w 1m where w is the view silhouette
Assume that the i-th sample is assigned to cluster k, then a(i) denotes the average distance of sample from other samples belonging to the same cluster k. We use d(i, k) to denote the average distance of sample from all samples belonging to cluster k and then b(i) = min d(i,, k).
coefficient and is calculated by क.th = = E w."
Global Clustering. K-means algorithm used to cluster H to obtain the global clustering center U [w' U1, w² ,,,... ; wh UM), where U denotes the global clustering center of the m-th view. One point worth noting is that { M is aligned in classes. Based on the Student-t distribution, the soft distribution for each sample is:
3.2.5. PRE-TRAINE TRAINING
Since there is no fused graph available at the first round of training, the clients need conduct pre-training to extract features for the server to perform graph fusion. For pre- training, the client only optimizes the local graph encoder for to obtain the local underlying features H,",, and uploads 11 to the server for graph fusion. The local graph recon- struction loss is used to optimize the local graph encoder S
Sty (1 14 U,1
(15)
15 1/, U, 1B
where h2 denotes the global features of the i-th sample and U, denotes the j-th global clustering center. By sharpen- ing the soft distribution, the pseudo-label P is obtained as follows:
S1y
ΣS
835
pree = [(T) - 1
(12)
Pij = Σ,
(16)
E
where AQT N| indicates the latent graph con- structed from H.T mentioned above. A E (0, NxN the incomplete local graph before global graph structure migration. (-)-) is a mask function that retain only the largest & elements in each row of the latent graph, and set the rest to zero. Only the strongest h edges are adopted to solve the incompleteness problem.
Finally, the clustering labels for each sample is obtained. The category ye for the i-th sample is:
y arg max Pik-
(17)
3.4. Algorithm optimization
Algorithm details the optimization process of FIMCFG consisting of two parts: clients and server. The client trains the local model in parallel, which pre-trains to extract local features before communication and uploads them to the server for graph fusion. In subsequent rounds of training, the clients extract the two-level features under the guidance of A and perform clustering with the supervision of P. The server fuses the features and latent graphs from the clients and performs global clustering to obtain the global clustering results. The client and server iterate alternately for T rounds.
3.3. Server Global Aggregation
The server receives the high-level features (Hm)), m= and weights {w from the clients and performs graph fusion, feature fusion and global clustering.
Graph Fusion. To fuse the graphs, the server extracts the latent graph {ÁT E [0, M J== from { M= by Eq. (1). After that, the server fuses the latent graph according to f
JM Jm=1- Specifically, it fuses the latent
graphs as follows:
4. Experiments
1 A- f-fM( M Σ(wm (w )))),,
(13)
4.1. Experimental Settings
1.1. DATASETS AND METRICS
where A E {0, 11NNNNN denotes the fused graph, @ denotes Hadamard product. fx(-) denotes the function that sets the largest k elements of each row in the matrix to and the rest to O. Wm E N denotes the weight matrix by broadcasting " E N
Our experiments were conducted on four widely used multi- view datasets. Specifically, Scene-15 (Lazebnik et al., 2006; Fei-Fei & Perona, 2005) consists of 4485 scene images clas- sified into 15 classes, with each sample represented by three
6

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2: Upload HM to the server. Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
Table Experimental results on four datasets. The best results in each column are shown bold and the second best results are underlined. à = indicates complete while = 0.5 indicates incomplete.
HW
Scene-15 15
Landuse-21
100Leaves
Methods
ACC
NMI
ARI
ACC
NMI
ART
ACC
NMI
ARI
ACC
NMI
ARI
FCUIF
0.965
0.923
0.925
0.471
0.444
0.293
0.274
0.302
0.130
0.910
0.964
0.878
FedMVFPC
0.407
0.528
0.329
0.304
0.327
0.173
0.201
0.205
0.074
0.187
0.574
0.137
HFMVC
0.789
0.732
0.666
0.367
0.381
0.217
0.237
0.272
0.096
0.709
0.880
0.624
DIMVC
0.446
0.533
0.381
0.350
0.309
0.179
0.243
0.301
0.109
0.825
0.922
0.753
DSIMVC
0.759
0.756
0.661
0.281
0.299
0.146
0.177
0.173
0.049
0.401
0.725
0.294
GIGA
0.807
0.853
0.756
0.221
0.263
0.041
0.131
0.257
0.017
0.742
0.877
0.483
GIMVC
0.935
0.886
0.874
0.426
0.465
0.279
0.258
0.337
0.114
0.857
0.952
0.819
CDIMC-net
0.861
0.890
0.827
0.347
0.421
0.198
0.184
0.236
0.054
0.799
0.938
0.751
MRL.CAL
0.478
0.526
0.337
0.194
0.168
0.069
0.163
0.169
0.045
0.224
0.587
0.126
Ours
0.976
0.946
0.948
0.475
0.472
0.309
0.304
0.350
0.153
0.960
0.982
0.942
FCUIF
0.917
0.840
0.827
0.410
0.378
0.234
0.232
0.244
0.098
0.604
0.764
0.431
FedMVFPC
0.313
0.364
0.192
0.203
0.199
0.071
0.134
0.123
0.025
0.134
0.397
0.030
HFMVC
0.516
0.433
0.272
0.221
0.216
0.096
0.166
0.181
0.047
0.325
0.622
0.177
DIMVC
0.322
0.255
0.151
0.310
0.261
0.143
0.226
0.278
0.099
0.579
0.731
0.380
0.5
DSIMVC
0.729
0.687
0.586
0.260
0.267
0.125
0.172
0.169
0.048
0.295
0.616
0.171
GIGA
0.764
0.730
0.594
0.146
0.127
0.008
0.182
0.279
0.025
0.418
0.649
0.055
GIMVC
0.911
0.838
0.825
0.385
0.373
0.218
0.228
0.273
0.085
0.688
0.842
0.555
CDIMC-net
0.858
0.861
0.792
0.217
0.268
0.067
0.122
0.161
0.020
0.330
0.643
0.207
MRL.CAL
0.358
0.370
0.191
0.189
0.150
0.065
0.162
0.168
0.044
0.145
0.434
0.052
Ours 0.952
0.897
0.896
0.444
0.420
0.264
0.293
0.323
0.139
0.802
0.886
0.696
Algorithm Optimization algorithm for FIMCFG
views. HandWritten (HW)) contains 2000 samples in ten numeric categories, each consisting of six views. Landuse- 21 (Yang & Newsam, 2010) consists of 2100 satellite images in 21 categories, 100 images per category, represented by three views. 1001eaves" consists of 1600 image samples of 100 plants, each represented by three different views. Details of the dataset are presented in Table 2.
Require:
Data with M views (Xm M m==1 distributed across M clients, number of clusters K, number of communi- cation rounds T, number of training rounds F.
Ensure:
Clustering results Y = {v1, "..... , yN 1: Pretrain the clients in parallel via Eq. (12).
3: Calculate the fused graph A.
In the federated learning settings, multiple views of these datasets are distributed across different clients, each contain- ing one of the views and isolated from each other. We define the data missing rate as 8 = N where No represents the number of overlapping samples in all clients and N represents the number of all samples in the dataset. To con- struct the incomplete dataset, we randomly selected N N. samples in the dataset, and set the n views of each sample among them to O, where n = [n'], }, [-] denotes rounding operation and
4: Distribute A to the clients.
5: while not reaching T do
6: for 1 to M in parallel do
7: Migrate the global graph structure.
8: while not reaching E do
9: Update n by optimizing Eq. (10).
10: end while
11: Use X to replace missing samples.
Calculate silhouette coefficients w".
U1, M 1).
Upload n and Th to the server.
14:
end for
We use three commonly-used metrics to evaluate the ef- fectiveness of clustering, i.e., clustering accuracy (ACC), normalized mutual information (NMI), and adjusted random index (ARI). A higher value of each metric indicates a better clustering performance.
15:
Update A by Eq. (13).
16:
Update H by Eq. (14).
17:
Obtain { Jmm1 by K-means.
LM
18:
Obtain P by Eq. (16).
Distribute A, {U"} 1= P clients.
20:
end while
hhupss//rcchve.ics wwwp::/wwww......dddd///////eaae www..aa....dtumm//aaattt//onooo.
21:
Calculate the clustering label by Eq. (17).

---

Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
PORAATTC
PPPDC
DWWC
-
PCUP
CORCC SKC
NML CXL
0.-
2.0
0.90
0.25
0.33
0.00
0.38
011
2.3
03
0.1
0.2
0.
0.2
2.
14 Naiing Pate
Missing Tats
Massnn Rale
Masinn Fate
160
(c)
(d)
Figure 2. Accuracy on four datascts with different missing rates. (a) HW, (b) Scenc- 15, (c) 100Lcaves, (d) Landuse-21.
Table 2. Description of datasets.
Datasets
Samples
Views
Distribution of dimensions
Classes
Scene-15 15
4485
3
20
59
40
15
HandWritten
2000
6
240
76
216
47
64
6
10
Landuse-21
2100
3
20
59
40
100leaves
1600
3
64
64
64
100
4.1.2. COMPARED METHODS
CDIMC-net( Wen et al., 2020) combines view-specific deep encoders and graph embedding strategy to capture the high-level features and local structure of each view.
In order to verify the effectiveness and superiority of our method, seven state-of-the-ar methods including three fed- erated MVC methods and six centralized IMVC methods are chosen to be compared methods. They are listed as follows:
ARRLCCAL(Wang et al., 2024a) utilizes joint learning of features in different subspaces for data recovery, consistent representation and clustering.
FCUIF(Ren et al., 2024) utilizes sample commonality, view versatility, and adaptive imputation techniques to address unaligned and incomplete data under federated setting.
We compared FIMCFG with baselines at two missing rate settings: O (complete) and & = 0.5 (incomplete).
FedMVFPC (Hu et al., 2024) is federated learn- ing method designed for fuzzy clustering.
4.2. Experimental Results
multiview
Table I shows the clustering results of FIMCFG against the compared methods both complete and incomplete scenarios. It can be observed that our method outperforms all the compared methods on all the datasets both see- narios, demonstrating the superiority of method. In partieular, in the missing settings, method significantly outperforms the second-ranked compared method on all the four datasets.
HFMVC (Jiang et al., 2024) a heterogeneity-wware federated deep multi-view clustering method that lever- ages contrastive learning to explore consistency and complementarity across multi-view data.
DIMVC(Xu al., 2022) is imputation-fice frree and fusion-free deep IMVC framework.
To further investigate the robustness of our method to the missing rate, we conducted experiments on four datasets with different missing rates ranging from 0.1 to 0.7 with an interval of 0.2. As shown in Figure our method outper- forms all the compared methods almost all the missing rate settings across all the four datasets, and this advan- tage becomes more and more significant as the missing rate increases. The results show that our method robust to different missing rates, and can use global information to estimate the data distribution even with high missing rates.
DSIMVC(Tang & Liu, 2022) proposes a bi-level op- timization framework that dynamically fills missing views using learned neighbor semantics.
GIGA(Yang et al., 2024) adaptively estimates the fac- tual weight of each available view to mitigate the ef- fects of missing views.
GIMVC(Bai et al., 2024) is a graph-guided, imputation- free incomplete multi-view clustering method.
8

---

Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
4.3.3. HETEROGENEITY ANALYSIS
312
In order to study the effect of the data heterogeneity in clients FIMCFG, we introduced the Dirichlet distribution in the construction of the incomplete dataset. The smaller the parameter a of Dirichlet, the larger the imbalance in the size of samples on the clients. We set up three sce- narios with different data distributions: a = 10 (High), a = 1.0 (Moderate) and random distribution (None) to illus- trate three heterogeneous scenarios. The results are shown in Figure 4, which shows that our model is less affected by data heterogeneity and performs well even in highly heterogeneous scenarios.
0375
122
0290
1.2
401
0220
310
0127
227
1277
48%
127
127
1071
3271
(a)
(b)
Figure 3. Parameter sensitivity analysis on Landuse-21 when 6
0.5. (a) Clustering performance with different values, (b) Clus- tering performance with different "/2 values.
4.3. Model Analysis
4. ABLATION STUDY
High
To further investigate the effectiveness of the global infor- mation in fused graph, we conducted an ablation study explore the impact of each component related to the fused graph clustering results. The components related to the fused graph include: (A) global graph structure migration, (B) feature fusion module, (C) global graph guidance, and (D) global graph encoder. We remove each component to explore effect. It should be noted that when we remove the global graph encoder, all these components will be not exist. The ablation study experimental results shown in Table 3. It can be easily seen that each module plays important role.
Moderate
None
0.8
0.6
0.4
0.2
HW
Scene-15 15
Landuse-21
100Leaves
Figure 4. Sensitivity to imbalanced sample sizes in clients on four datasets with missing rate 0.5.
Table Ablation study Scene-15 when 8 0.5.
Components
Scene-15
A
B
C
D
ACC
NMI
ART
0.431
0.415
0.258
5. Conclution
V
0.357
0.407
0.205
In this paper, propose novel federated incomplete multi- view clustering method FIMCFG. We designed the global graph structure migration to correct the local graphs and es- timates the missing features with GCN to tackle the missing data problem. Moreover, we designed the dual-head graph encoder and fusion module extract high-level features by using the fused global graph. Experimental results verified the effectiveness and superiority of FIMCFG
L
v
0.412
0.414
0.251
0.247
0.210
0.095
0.444
0.420
0.264
4.3.2. PARAMETER ANALYSIS
During the training of clients, the total loss function defined by Eq. (10) has two hyperparameters ? and 72 to trade-off the graph reconstruction loss and content reconstruction loss.
A Acknowledgments
We conducted the experiments with various settings of the two hyperparameters ranging from 10-3 to 10 at 0.5, as shown in Figure 3. We observe that 71 in the range of [10-2 10 is robust for clustering results. Too small ? degrades the clustering performance due to the fact that it makes the encoder to ignore the global information of the fused graph. The clustering performance is not sensitivity to 7/2. Based on the experimental results, we recommend setting 7/ to I and 72 to 0.1 for optimal performance.
This work is supported in part by the National Natural Science Foundation of China (No. 62276079). Young Teacher Development Fund of Harbin Institute of Technol- ogy IDGA10002071, Research and Innovation Foundation of Harbin Institute of Technology IDGAZMIZ00210325, Key Research and Development Plan of Shandong Province 2021SFGC0104 and the Special Funding Program Shan- dong Taishan Scholars Project

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Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
Impact Statement
Recognition (CVPR'05), volume 2, pp. 524-531 vol. 2, 2005. doi: 10.1 N0.1109/CVPR.2005.16
This paper presents work whose goal to advance the field of Machine Learning. There are many potential societal consequences of our work, none of which feel must be specifically highlighted here.
Hu, X., Qin, J., Shen, Y., Pedrycz, W., Liu, X., and Liu, J. An efficient federated multiview fuzzy c-means clustering method. IEEE Transactions on Fuzzy Systems, 32(4): 1886-1899, 2024. doi: 10. 10.110//FUU2..2233333335
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Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
A. More Experiments of FIMCFG
A.I. We show more ablation study results on HW, Landuse-21 and 100Leaves in Table 4.
Table 4. Ablation study results on HW, Landuse- 21 and Scene-15 15 with ò 0.5.
Components
HW NMI
Landuse-21
100Leaves
A
B
C
D
ACC
ARI
ACC
NMI
ARI
ACC
NMI
ARI
V
0.950
0.896
0.892
0.252
0.277
0.114
0.758
0.870
0.650
V
d
0.930
0.889
0.876
0.213
0.284
0.084
0.792
0.885
0.687
V
/
V
0.912
0.865
0.847
0.282
0.325
0.138
0.782
0.880
0.679
0.556
0.543
0.366
0.175
0.179
0.051
0.430
0.666
0.231
V
0.952
0.897
0.896
0.293
0.323
0.139
0.802
0.886
0.696
A.2. We show the experimental results in NMI and ARI for all the methods under different missing rates ranging from 0.1 to 0.7 with an interval of 0.2. The results are shown in Figure 5 and Figure 6, respectively.
73KC0O
PoPAVTCC
MPMAC
995A
CORIC
CAL
DSEVVC
0.5
0.30
0.23
0.20
04-
0.50
kasing
Masting
Masing Fabe
Figure 5. NMT on four datasets with different missing rates. (a) HW, (b) Scene- 15, (c) 1001 laaves, (d) Landuse-21.
FICNO
IFVIC
DAWC
FOUF
05A
NAL_CA)
0..55
0.32
0.22
00-
3.5
2
0.79
02
0.32
Maving
Mauiig
Masing Pado
[c)
(d)
Figure 6. ARI on four datasets with different missing rates. (a) HW, (b) Scene- 15, (c) 100Lcaves, (d) Landuse-21.
A.3. We show more parameter analysis experiments on HW, Scene-15 and 100Leaves data sets. Parameter sensitivity analysis of 7/ are shown in Figure Parameter sensitivity analysis of 72 are shown in Figure 8.
12

---

Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidance
ACC
345
0.94
AR1
TIMI
Clusenng Perfomance
Elaatoin nafommnne
340
0.80
ACC
0.70
10
10
30
100
107
102
102
10
30
10
10P
101
302
307
10
10
30
300
101
102
107
(a)
(b)
(c)
Figure 7. Parameter sensitivity analysis of 1/1 when 0.5 on three data sets: (a) HW, (b) Scene- 15, (c) 100Leaves.
0.95
ACC
0.99
ARI
Clattiing Pafammenee
3.10
ACC
2.00
0.90
2.30
0.09
0.20
HHI
AM
10"1
1277
3071
107
1.37
107
157
1013
12.2
10"3
102
101
707
707
1071
1077
3071
307
101
107
107
(a)
(b)
(c)
Figure Parameter sensitivity analysis of 7/2 when 0.5 on three data sets: (a) HW, (b) Scene- 15, (c) caveesves.
13


Reviews:
[{"review_id": "CWgU7Fp1jm", "reviewer": "Reviewer_KxVe", "summary": "The work proposes a novel GCN-based federated incomplete multi-view clustering framework. The information propagation limitation problem is solved by introducing the globally fused graph guidance when extracting features. The global graph structure migration is proposed in this paper. The incomplete data problem is solved by repairing the incomplete local graph with the fused graph. An adaptive weighted aggregation approach is developed, which could automatically adjust the importance of each view when conducting feature fusion and graph fusion. Experimental results demonstrate the effectiveness of the proposed method.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R1：Under the effect of the GCN encoder on clients, each sample estimates its single-view features using its own attribute values and those of its neighboring nodes. Since we fill the missing samples with zeros vector, the encoder automatically ignores these missing samples during computation. However, for the missing samples, we use similarity-based calculations for the local adjacency matrix. As a result, the missing samples (represented as zero vectors) are not adjacent to any complete samples, making it impossible to compute their features. To tackle this issue, we propose global graph structure migration, where the rows corresponding to missing samples in the local adjacency matrix are replaced with rows of the global adjacency matrix that integrates multi-view information. This complements the local adjacency matrix with the graph structure of missing samples, enabling them to estimate their features using adjacent complete samples.\nR2：Thank you for your suggestion. We will add the dataset size, the number of views, and the dimensions of each view in tabular form in the subsection 4.1.1 of the revised version.\nR3：Thanks for your advice. We'll add the following works to enrich the federated multi-view clustering related works.\n[1] Hu X, Qin J, Shen Y, et al. An efficient federated multiview fuzzy c-means clustering method[J]. IEEE Transactions on Fuzzy Systems, 2023, 32(4): 1886-1899.\n[2] Huang S, Shi W, Xu Z, et al. Efficient federated multi-view learning[J]. Pattern Recognition, 2022, 131: 108817.\n[3] Chen X, Ren Y, Xu J, et al. Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views[J]. Advances in Neural Information Processing Systems, 2024, 37: 37020-37049.", "claims_and_evidence": "The claims of the paper are well supported by the experimental results.", "methods_and_evaluation_criteria": "The proposed federated incomplete multi-view clustering method effectively tackles the problems of global information exploration and missing data in federated multi-view clustering tasks.", "theoretical_claims": "The proposed method, which includes Client Local Training with Global Guidance and Server Global Aggregation, is correctly designed and implemented.", "experimental_designs_or_analyses": "The experiments are conducted on four widely used multi-view datasets, providing comprehensive and sufficient results.", "relation_to_broader_scientific_literature": "This paper is closely related to existing studies on federated multi-view clustering and builds upon them by proposing a novel globally fused graph guidance method.", "essential_references_not_discussed": "The references in this paper are relatively sufficient, but it may be beneficial to consider citing some recent studies on federated multi-view learning.", "other_strengths_and_weaknesses": "Strengths:\n1. The idea of dual-head graph convolutional encoder to extract the features and globally fused graph guidance is novel and interesting.\n2. The proposed method deals with the missing data well, demonstrating strong performance even with high missing data rates, making it suitable for real-world applications where data incompleteness is common.\n3. By operating in a federated learning framework, the method ensures that raw data remains on local devices, addressing the data privacy concerns.\n4. The authors conduct extensive experiments on multiple datasets, demonstrating the superiority of their method over existing approaches.\n\nWeaknesses:\n1. How the  global graph structure migration and encoders solve the incomplete problem should be explained in detail.\n2. It is suggested to introduce the data statistics in the experiments in a table.\n3. More federated multi-view clustering works are suggested to introduced to make the related works rich.", "other_comments_or_suggestions": "Please refer to the aforementioned strengths and weaknesses.", "questions_for_authors": "Please refer to the aforementioned weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xD0TatPXRt", "reviewer": "Reviewer_UkiW", "summary": "The authors proposed a federated incomplete multi-view clustering framework named FIMCFG. It designed a dual-head graph convolutional encoder at the client to extract the global and view-specific information. With the guidance of the fused graph, high-level features are used to conduct clustering under the supervision of pseudo-label. As the federated learning framework, it preserves the privacy well. Incomplete data problem is addressed with the fused graph and graph convolutional operation. The idea of this work is novel. It offers a tool to deal with incomplete multi-view clustering in federated learning framework. The experiments show the effectiveness and superiority of the proposed method.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R1-4: Thanks for your acknowledgement.\nR5: Silhouette comprehensively considers the similarity between samples within a cluster and the distance between different clusters. It evaluates the  clustering quality based on two factors: cohesion and separation. Silhouette ranges between [-1, 1], where a value close to 1 indicates that the samples are well clustered within their respective cluster and are well separated from other clusters, demonstrating good clustering performance. Conversely, a value close to -1 suggests that the samples may have been incorrectly assigned to clusters, resulting in poor clustering performance. We will include the aforementioned explanation of the silhouette in the revised Section 3.2.4 and provide a detailed exaplanation of within-cluster cohesion, between-cluster separation, and the formula for silhouette.\nR6: Thanks for your useful suggestion. We'll add some recent related works on incomplete multi-view learning and discuss them in the final version if it is accepted.\nR7: Thanks for your advice. We'll check throughout the whole paper and improve the language.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes, the authors adopted several evaluation metrics (ACC, NMI, ARI) for performance evaluation.", "theoretical_claims": "The authors mainly evaluated the work via a number of experiments.", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "The authors explored the federated incomplete multi-view clustering by developing a better way for global information fusion.", "essential_references_not_discussed": "None.", "other_strengths_and_weaknesses": "Strength:\n1. Besides clustering phase, the global information are also mined in feature extracting with the designed dual-head GCN encoder. This is interesting and novel.\n2. The global graph structure migration is proposed to fill in incomplete local graph, which is further used to estimate the missing values in original data.\n3. This work considers the missing data and data privacy problems together.\n4. The experiments are rich. Besides compared experiments, ablation study, parameter analysis, the authors conducted the experiments with data under different missing rate to show its performance. In addition, The heterogeneity analysis shows the methods’ performance on different data heterogeneity scenarios, this is important in federated learning.\nWeakness:\n1. Silhouette should be explained in detail.\n2. Some related works on incomplete multi-view should be added and discussed.\n3. The expression needs further polishing.", "other_comments_or_suggestions": "Despite key works are discussed, it is recommended to conduct a wider range of literature reviews.", "questions_for_authors": "Refer to the weakness.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SAK1oqqH42", "reviewer": "Reviewer_fzVj", "summary": "This paper provides a federated incomplete multi-view clustering approach to solve incomplete data and data privacy problem. Dual-head graph convolutional encoder is designed to extract the underlying features, the global graph structure migration is designed to repair incomplete local graphs to estimate the missing feature further. The extensive experimental comparison and analysis demonstrated the proposed approach works well and performs better than compared state-of-the-art methods.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R1: Thanks for your helpful advice. The fusion module on all clients brings the fused high-level feature’s graph structure closer to the global graph. Conversely, the global graph is updated collaboratively by all clients. Through such iterations, the model converges to a stable state, eventually eliminating conflicting parts.\nR2: The missing features are automatically estimated utilizing the GCN encoder and the global graph structure migration, as shown in the response R1 to Reviewer 1. Our content reconstruction loss is primarily designed to enhance the robustness of the model and to learn the distribution of missing samples.\nR3: Thank you for your useful suggestions. We will rectify the errors  in the revised version and conduct a thorough check.\nR4: Thanks for your good advice. We'll add some more experiments or some deep discussion in the revised version.", "claims_and_evidence": "The authors provide experiments and code to support the proposed method.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "I checked the formulas in the methods section and they are all correct.", "experimental_designs_or_analyses": "The experiments are varied and include performance analysis and model analysis. The selected datasets used are common multi-view datasets, but I would like to see more experiments on large-scale datasets and more federated multi-view approaches for comparison.", "relation_to_broader_scientific_literature": "This paper is useful for further exploration of federated multi-view clustering.", "essential_references_not_discussed": "There are a few field-related papers that are not cited:\n[1] An efficient federated multi-view fuzzy c-means clustering method.\n[2] Efficient federated multi-view learning.\n[3] Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations.", "other_strengths_and_weaknesses": "Strengths:\n(1) By introducing the globally fused graph to guide the upstream feature extraction process, the model could exploit the global information when extracting features in distributed environment, which improves the clustering performance.\n(2) With the global graph structure migration, the model is robust to different missing rates.\n(3) The experiment in the work is sufficient to prove the validity of the model.\n(4) The technical details of the method are detailed.\n\nWeaknesses:\n(1) How the fusion module deals with the conflicting components should be illustrated in detail. This can help the readers to understand this point well.\n(2) How to recover the missing feature values needs more illustration. Is it implemented by optimizing the content reconstruction loss? \n(3) The writing of this paper can be improved. For example, Eq (2) is out of the content area. The singular and plural forms of some words are wrong. \n(4) More experiments on large-scale datasets and more federated multi-view approaches for comparison are desired.", "other_comments_or_suggestions": "See Weaknesses.", "questions_for_authors": "See Weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2xUB0EI2Fn", "reviewer": "Reviewer_RBHX", "summary": "This paper presents a novel Federated Incomplete Multi-view Clustering method with globally Fused Graph guidance (FIMCFG), addressing the challenges of privacy preservation and data incompleteness in federated multi-view clustering framework. The main contribution of this work lies in its novel approach to handling incomplete multi-view data in a distributed setting while leveraging global information to improve the clustering performance.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "R1: Thanks for your good advice. Incomplete multi-view clustering are introduced in GNN based multi-view clustering and federated multi-view clustering. To make it more clear, we can extract these and introduce them together.\nR2: Thanks for your careful check. We'll correct all these errors or typos in the revised version if it is accepted finally.\nR3: Thanks for your helpful suggestion. During communication, the server transmits the fused graph  to the client to guide the upstream feature extraction process, the pseudo labels P to guide the training of the downstream clustering layer, and the cluster centers  to align the clustering layers of different clients. Meanwhile, the client uploads high-level features  and their corresponding weights  to the server for aggregation. We will elaborate further  on this in Subsection 3.2.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes. The metrics, including ACC, NMI and ARI, are commonly used in clustering analysis.", "theoretical_claims": "N/A. There is no theoretical claim.", "experimental_designs_or_analyses": "Yes. I have checked the comparison to existing methods, ablation study, parameter analysis and heterogeneity analysis. They well support the claims.", "relation_to_broader_scientific_literature": "This paper propose a novel approach to group the incomplete multi-view data in a distributed setting. This is important but lacks exploration in depth in literature. So there would be a broad impact.", "essential_references_not_discussed": "[1] also explores the federated multi-view clustering method over distributed data. Their commons and differences may be worth to be introduced and discussed. \n\n[1] Liu et al. Active-Passive Federated Learning for Vertically Partitioned Multi-view Data. Arxiv 2024.", "other_strengths_and_weaknesses": "Strength:\n\n1. The design of dual-Head graph convolutional encoder is novel and interesting.\n\n2. To address the missing data issue, the authors introduce a global graph structure migration technique. This method repairs the incomplete local graphs by leveraging the global graph structure, enabling the estimation of latent features to recover the missing data.\n\n3. The paper is well organized and easy to follow.\n\nWeaknesses:\n\n1. Related works on incomplete multi-view clustering lacks: Incomplete multi-view clustering works should be introduced, since it is quite related to the proposed method.\n\n2. The writing expression needs improvement: Although the paper organization is good, the paper writing still needs improvements. There are some grammar errors or typos, such as “Most” in “In summary, Most  of these approaches” in the third paragraph of Section Introduction should be “most”, “client extract underlying features” in caption of Figure 1 should be “client extracts underlying features”, please check throughout the whole paper and correct them.\n\n3. Interaction between clients and server is not clear: What information are exchanged in Subsection “3.2. Client Local Training with Global Guidance” should be made more clear.", "other_comments_or_suggestions": "Please see Section **Other Strengths And Weaknesses**", "questions_for_authors": "Please see Section **Other Strengths And Weaknesses**", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper addresses an important challenge in federated multi-view clustering, namely the dual issues of incomplete data and privacy preservation in distributed settings. The authors propose a method that leverages a dual-head graph convolutional encoder to extract both global and view-specific features. A key novelty is the use of a globally fused graph to guide feature extraction and repair incomplete local graphs, thereby facilitating effective clustering under both complete and missing data conditions. Experimental evaluations on several widely used multi-view datasets, across varying missing rates, reinforce the proposed method’s superiority over a range of state-of-the-art techniques.

All reviewers have converged toward a positive evaluation, with overall recommendations ranging from “Accept” (score of 4) to “Weak Accept” (score of 3). Based on the strength of the contributions, the novelty in addressing both privacy preservation, data incompleteness, and robust experimental validations, the meta-review recommends acceptance of this paper.
Decision: Accept (poster)