Federated GNNs for EEG-Based Stroke Assessment

Authors: andrea  protani
^lorenzo  giusti
albert  sund aillet
simona  sacco
paolo  manganotti
lucio  marinelli
\  diogo
reis  santos
pierpaolo  brutti ^pietro caliandro
luigi  serio
marco  fumero
clementine  domine
zorah  lähner
donato  crisostomi
luca  moschella
kimberly  stachenfeld
Venue: NeurIPS_cc_2024_Workshop_UniReps
Published: 2024-09-24 12:09:02

Abstract: Machine learning (ML) has the potential to become an essential tool in supporting clinical decision-making processes, offering enhanced diagnostic capabilities and personalized treatment plans. However, outsourcing medical records to train ML models using patient data raises legal, privacy, and security concerns. Federated learning has emerged as a promising paradigm for collaborative ML, meeting healthcare institutions' requirements for robust models without sharing sensitive data and compromising patient privacy. This study proposes a novel method that combines federated learning (FL) and Graph Neural Networks (GNNs) to predict stroke severity using electroencephalography (EEG) signals across multiple medical institutions. Our approach enables multiple hospitals to jointly train a shared GNN model on their local EEG data without exchanging patient information.  Specifically, we address a regression problem by predicting the National Institutes of Health Stroke Scale (NIHSS), a key indicator of stroke severity. The proposed model leverages a masked self-attention mechanism to capture salient brain connectivity patterns and employs EdgeSHAP to provide post-hoc explanations of the neurological states after a stroke. We evaluated our method on EEG recordings from four institutions, achieving a mean absolute error (MAE) of 3.23 in predicting NIHSS, close to the average error made by human experts (MAE $\approx$ 3.0). This demonstrates the method's effectiveness in providing accurate and explainable predictions while maintaining data privacy.

Main paper: 
 introduction: 
 Neurological evaluation involving brain signals is the primary tool in assessing and managing stroke patients [1, 2, 3] . These evaluations provide insights into the functional state of biological neural networks affected by stroke, which are essential for guiding clinical decisions and rehabilitation strategies [4, 5, 6] . However, the susceptible nature of such data poses significant challenges, particularly regarding privacy and security [7] . These challenges often hinder data sharing across institutions, limiting the collaborative potential for advancements in clinical neuroscience research and the development of robust predictive models [8, 9] . Federated learning (FL) has emerged as a promising solution to these challenges by enabling multiple institutions to collaboratively train models without sharing sensitive data (Fig. 1 ) [10, 11] . This approach preserves privacy while leveraging the collective power of diverse datasets. This work proposes a federated learning framework based on the Message Queuing Telemetry Transport (MQTT) communication protocol to predict stroke severity using Graph Neural Networks (GNNs) on brain networks extracted from EEG data. GNNs are particularly suited for this task due to their ability to model complex relationships within brain networks, capturing both structural and functional connectivity patterns to provide a better assessment of the stroke impact on brain regions [12, 13, 14, 15] . Moreover, the integration of explainability mechanisms enhances the interpretability of the model, which is of primary importance in clinical settings where understanding the rationale behind networks' decisions is critical.This study utilizes a comprehensive dataset comprising EEG recordings from 72 patients collected during hospitalization across four medical centers. The EEG data were analyzed across various frequency bands to construct brain connectivity graphs. The distribution of patients among the hospitals is shown in Fig. 2 . A standardized data collection protocol was implemented across all hospitals. EEG signals were recorded at rest, with patients' eyes closed, for at least 5 minutes during their stay in the stroke unit. The recordings were obtained using 31 electrodes positioned according to the international 10 -10 system, with a common reference electrode placed on the mastoid and a ground electrode.The EEG data were preprocessed with a band-pass filter between 0.2 and 47 Hz at a 512 Hz sampling rate, followed by artifact removal through Independent Component Analysis (ICA) [16] . Next, eLORETA [17] was employed to reconstruct whole-brain sources and calculate Lagged Linear Coherence (LLC) graphs for the first five frequency bands: δ (2 -4 Hz), θ (4 -8 Hz), α 1 (8 -10.5 Hz), α 2 (10.5 -13 Hz), and β 1 (13 -20 Hz) [4] .The NIH Stroke Scale (NIHSS) is an objective assessment tool to quantify the impact of stroke-related events. Initially intended to evaluate the effects of intervention in clinical trials, it has since become a standard tool in clinical and emergency settings for assessing stroke severity. It is an integer value between 1 and 42, which quickly determines the level of neurological impairment and guides treatment decisions by systematically evaluating factors such as consciousness, visual fields, motor function, and language ability. Despite the growing interest in applying graph theory to brain graphs in the context of neuroscience [18, 19, 20, 21] , existing approaches often fall short in addressing the unique challenges posed by EEG data. Traditional models lack the structured inductive bias required to capture the connectivity patterns inherent in EEG data [22, 23, 24, 7] . Our approach addresses these limitations and ensures that collaborating institutions maintain data privacy [25, 26, 27] . In particular, our approach leverages explicitly a multilayer GNN to capture insights from graphs extracted from the five different EEG frequency bands defined above. The multilayer network representation allows for integrating information from various neural oscillatory patterns, enabling the model to learn interactions across different frequency bands and enhancing its ability to predict stroke severity.Contributions In this work, we design a federated learning framework building upon recent work on Multilayer Dynamic Graph Attention Networks for neurological assessments [28] to predict stroke severity from EEG signals in a privacy-preserving setup. This enables multiple healthcare institutions to unify multiple graph representations. We evaluate both FedAvg [10] and SCAFFOLD [29] algorithms, comparing their performance in handling the statistical heterogeneity inherent in multiinstitutional EEG data. Our federated learning system integrates MQTT as an efficient communication protocol, demonstrating its security in dispatching model updates and aggregation across distributed clients. We validate our method approach on a dataset of EEG recordings from 72 stroke patients across multiple institutions, demonstrating its effectiveness in providing accurate predictions while ensuring data privacy and robustness. Finally, we provide interpretable explanations of model decisions using EdgeSHAPer [30] to allow for transparent and trustworthy decisions of our predictive framework. 
background and related works: 
 This section reviews the concepts of GNNs and FL and discusses previous studies related to EEGbased stroke assessment.GNNs operate on graph-structured data, where a graph G = (V, E) consists of nodes v ∈ V and edges (u, v) ∈ E. Each node v is associated with a feature vector x v ∈ R d . GNNs can be formally defined as functions of the form GNN w : (G, {x v } v∈V , w) → y G , where w represents a set of trainable parameters. The core idea behind GNNs is to learn node or graph-level representations by iteratively updating node features through message passing:EQUATIONwhere h l v represents the hidden feature vector of v at layer l, and h 0 v = x v , while N (v) is the set of neighbors of v. For the edge between u and v, h uv represents its hidden feature vector. The function com updates the hidden feature vector with messages received from u ∈ N (v), agg is a permutation-invariant aggregation function and M is a message function.Graph Attention Networks GATv2 [31] introduces a self-attention mechanism to account for the varying importance of neighboring nodes during message passing. For a node v and its neighbor u ∈ N (v), the attention scores w (x v , x u ) is computed as s w (x v , x u ) = a ⊤ σ (W [x v ∥ x u ]), where W is a learnable weight matrix, a is a learnable vector of attention coefficients, σ is the LeakyReLU activation function and ∥ denotes concatenation. The attention scores are then normalized via α v,u = softmax u∈N (v) (s w (x v , x u )).To represent brain connectivity across different frequency bands, a multi-layer graph approach has been proposed [28] . The multi-layer graph Ḡ, illustrated in Fig. 3 , is defined as Ḡ = {G α1 , G α2 , G β1 }, where each G i = (V, E i ) represents a graph for a specific frequency band, sharing the same vertices Figure 3 : Illustration of the multi-layer network structure. Each layer corresponds to a specific frequency band, with inter-layer connections helping cross-frequency information flow [28] .V but with unique edge sets E i . Inter-layer edges E inter connect nodes across different layers, allowing cross-frequency communication.Rewiring Brain Networks To address the issue of over-smoothing [32, 33] in fully connected graphs when using GNNs, a rewiring strategy has been developed [28] . This process transforms the graph G l = (V, E l ) of each brain network layer into a sparse graph G ′ l = (V, E ′ l ) through structural and functional rewiring.(a) Structural Rewiring: a spatial proximity function ϕ :V × V → R + is defined based onEuclidean distance between Brodmann areas. For each node v ∈ V, the k = 3 spatially closest nodes are selected.(b) Functional Rewiring: a function ψ l : E l → R is defined, mapping each edge to its LLC value in layer l. Edges above the 99 th percentile of LLC values are retained. The final set of edges in the rewired graph for each layer isE ′ l = E ϕ ∪ E ψ ∪ {(v, v) : v ∈ V}, where E ϕ represents structural connections, E ψ represents functional connections, and self-loops are included.This rewiring strategy reduces edge density (retaining only ≈ 5% of the initial number of edges) while preserving critical functional and structural information, enhancing the GNN's ability to learn from the underlying brain network topology.The proposed GNN architecture, combined with the multi-layer graph representation and rewiring strategy, allows effective learning from the complex, sparse brain networks derived from EEG data.FL is a paradigm that enables collaborative model training across a federation of multiple institutions [10, 34] . This approach has gathered interest in domains like digital healthcare, where regulations and ethical considerations often restrict data sharing [8] . Formally, let D ci represent the local dataset on client c i and W denote the global model parameters. Each client c i updates the global model by minimizing a local loss function L ci (W; D ci ). The global objective can be expressed as:EQUATIONwhere n i is the number of samples on device c i and n = N i=1 n i is the total number of samples across all devices.Federated Averaging (FedAvg) is one of the most popular algorithm for FL aggregation [10] . FedAvg aims to address the challenges of statistical heterogeneity among participants in federated systems. The algorithm operates as follows: the server initializes the global model parameters W 0 . Then, for each round r = 1, . . . , R, the server samples a subset of clients S r according to a probability distribution P and sends the current global model W r-1 to each of the clients in the sampled subset. Each client c i ∈ S r updates the model locally using their dataset D ci by computing W r ci = OPT(W r-1 , Dc i ), where OPT is the local optimization algorithm (e.g., SGD, Adam, RMSProp). The clients then send their updated models back to the server. The server aggregates these local models to update the global model using a weighted average:EQUATIONwhere n ci = |D ci | and n Sr = ci∈Sr n ci . This process repeats for R rounds, resulting in the final global model W * = W R . FedAvg unifies the representations learned by individual clients on their local datasets into a single global model by aggregating their locally updated models. This process effectively captures patterns and features from each client's data, integrating them into a single model that generalizes across the entire dataset distribution. This way of averaging local models allows FedAvg to unify the collective knowledge of all clients into a single representation while preserving data privacy, resulting in an improved global model without direct access to the client's raw data.Stochastic Controlled Averaging for Federated Learning (SCAFFOLD) is an algorithm proposed in [29] to address the client drift problem in heterogeneous settings. SCAFFOLD introduces control variates to correct for the drift in local updates. thereby aligning the clients' learning processes more closely with the global objective. Initially, the server initializes the global model parameters W 0 , the server control variate c 0 and sets all client control variates c 0 ci = 0 for each client c i . For each round r = 1, . . . R, the server samples a subset of clients S r according to a probability distribution P and sends the current global model W r-1 along with the server control variate c r-1 to the selected clients.Each client c i ∈ S r initializes their local model with the global model: W r,0 ci = W r-1 . The client then performs K local update steps. At each local step k = 1, . . . K, the client updates the model as:EQUATIONwhere η l is the local learning rate,∇F ci W r,k-1 ciis the gradient of the local loss function for client c i , c r-1 ci is the client control variate, and c r1 is the server control variate. After completing the local updates, the client computes the model update:∆ i = W r ci -W r-1, and updates its control variate as:EQUATIONThe client then returns ∆ i , and the updated control variate c r ci to the server. Upon receiving the updates from all participating clients, the server aggregates the update to update the global model and the server control variate:EQUATIONThis process repeats for R rounds, resulting in the final global modelW * = W R .SCAFFOLD enhanced model aggregation over FedAvg by utilizing control variates to correct for client drift caused by data heterogeneity. In particular, adjusting local updates with these control variates allows SCAFFOLD to ensure that the federated learning process aligns clients' representation into a unified global model that remains synchronized with the global objective, mitigating undesired effects of non-I.I.D. data distributions due to statistical heterogeneity. Consequently, SCAFFOLD is a theoretically sound algorithm that aims to achieve a more accurate and generalized global model that unifies the collective knowledge of all clients while preserving data privacy. It is also worth emphasizing that if an early stopping mechanism is implemented for FedAvg and SCAFFOLD, the training may end at an earlier round r ′ < R when certain convergence criteria are met (e.g., the validation loss stops decreasing). In this case, the final global model becomes W * = W r ′ .While the model in [28] effectively predicts stroke severity from EEG data, its interpretability is limited to the noise introduced by random initialization of the attention coefficients a. In particular, the functional communication between brain regions allows clinicians to understand why the model makes specific predictions, allowing clinicians to trust and utilize these insights in personalized treatment plans. Traditional attention mechanisms provide some interpretability by highlighting important nodes and edges, but they often lack a theoretically grounded measure of each edge's contribution to the prediction.To address this gap, we incorporate EdgeSHAPer [30, 35] , as an edge-centric explanation method based on the Shapley value concept from cooperative game theory [36] . EdgeSHAPer assigns a Shapley value to each edge in the brain network, quantifying its contribution to the model's output. This method uses a principled way to assess the importance of individual neural connections, thereby offering more profound insights into the brain's functional reorganization after a stroke.EdgeSHAPer estimates the Shapley value ϕ(e i ) for each edge e i in the graph G, representing the average marginal contribution of e i to the model's prediction over all possible subsets of edges. The Shapley value for an edge is defined as:EQUATION)where E is the set of all edges in G, S is a subset of edges not containing e i , and f (S) is the model's prediction when only the edges in S are present. The factorial terms account for all possible orderings of edges, ensuring a fair attribution of importance. Computing the exact Shapley values is computationally intractable for graphs with many edges due to the combinatorial number of subsets. Therefore, we employ a Monte Carlo approximation [37] :EQUATIONwhere M is the number of sampled subsets S k ⊆ E \ {e i }. We generate these subsets by random sampling, ensuring that each edge's contribution is estimated over diverse contexts.The impact of acute stroke on the topology of cortical networks has been extensively investigated through EEG analysis, revealing significant, frequency-dependent alterations in network properties. Specifically, stroke leads to decreased small-worldness in the δ and θ bands and increased smallworldness in the α 2 band across both hemispheres, regardless of lesion location [1] . Distinct modifications in functional cortical connectivity due to acute cerebellar and middle cerebral artery strokes have been highlighted, showing different impacts on network architecture and small-world characteristics across various EEG frequency bands, independent of ischemic lesion size [4, 19] .Additionally, research has shown that acute cerebellar and middle cerebral artery strokes distinctly affect functional cortical connectivity, with significant differences in EEG-based network remodeling across δ, β 2 , and γ frequency bands, highlighting the unique impact of stroke location on brain network dynamics [19] . The prognostic role of hemispherical differences in brain network connectivity in acute stroke patients has been explored using EEG-based graph theory and coherence analysis. Findings indicate that stroke-induced alterations in network architecture can predict functional recovery outcomes, providing a basis for tailored rehabilitation strategies [24, 23] .The relevance of brain network analysis for stroke rehabilitation has been studied, highlighting the potential of network-based approaches to inform and guide therapeutic interventions in stroke recovery [38] . Dynamic functional reorganization of brain networks post-stroke has been emphasized, providing critical insights into the brain's adaptive mechanisms following a stroke and supporting network analysis to understand structural and functional reorganization [3] . Finally, changes in the contralesional hemisphere following stroke and the implications of the stroke connectome for cognitive and behavioral outcomes have been explored, enhancing our understanding of the complex network dynamics involved in stroke pathology and recovery [2, 39, 20] 3 Experimental setupWe designed two distinct federated learning setups to investigate how data distribution affects model performance. The first configuration mimics real-world conditions by treating individual hospitals as clients in the federation, reflecting the natural data distribution within healthcare systems. We curated three evenly distributed datasets in the second setup, creating a more controlled, idealized scenario. By comparing these two approaches, we assess whether having a representative distribution of the entire dataset at each client significantly impacts the learning outcomes. All experiments were conducted using the same model architecture and training hyper-parameters. We used a batch size of 2 and applied the mean squared error (MSE) loss function. We implemented gradient clipping with a threshold of 10 to avoid exploding gradient issues. The model's parameters were optimized using the Adam optimizer with a learning rate of 0.003 and a weight decay of 0.01 to prevent overfitting.For both setups, we fixed a test set by randomly sampling 11 elements across the clients to be representative of the overall data distribution, ensuring consistency in the evaluation process and reducing the risk of bias. We used M = 100 for the Monte Carlo approximation in 9 to estimate Shapley values efficiently.The model architecture was built on top of the GATv2 message-passing scheme [31] . The core GNN consisted of 3 GATv2 layers, each with 6 attention heads. To reduce overfitting, we incorporated dropout with a 0.1 probability of randomly deactivating neurons during training and used the ReLU activation function for non-linearity. A mean pooling layer was employed as the readout operation following the final GNN layer.At the core of federated learning systems lies the need for efficient communication protocols to maintain model performance under resource constraints [10, 40, 41] . Ensuring efficient and secure data exchange among distributed actors of federated systems requires carefully tailoring the protocol for communicating models' parameters among the network nodes. We chose the MQTT protocol instead of HTTP REST for our setup due to superior bandwidth efficiency, lower latency, and low overhead, making it suitable for low-power IoT devices [34] . On top of the aforementioned benefits, MQTT offers one-to-many communication, which is the central point when distributing global models during FL rounds. It also supports large message payloads (with fragmentation if necessary), serialization, and compression. The communication network orchestrates FL processes through MQTT's publish-subscribe mechanism. Clients (healthcare institutions) exchange model parameters and configurations by publishing their local models and downloading global models via specific MQTT topics [42, 43] .This architecture handles both synchronous (all clients wait for each other) and asynchronous (clients act independently of others) FL operations. For the FL orchestration, there are four different setups: (c) Both PS and clients are asynchronous, where the PS aggregates models at regular intervals, regardless of client completion.(d) Fully decentralized and asynchronous, clients exchange models directly via MQTT without a central PS.We chose option (a) for our experimental setup since we prioritize stability and consistency in the model aggregation process due to the small data samples. All MQTT exchanges are encrypted via TLS, and the MQTT broker manages local models until training is completed. The flexible architecture supports dynamic scaling based on task complexity and computational resources [44] .Comparative Analysis: Our experimental framework spans several learning paradigms, allowing us to comprehensively evaluate the performance of FL in the context of GNNs. The scenarios explored include: This approach allows us to: In all experiments we used a machine with an NVIDIA ® RTX 3090 GPU with an Intel ® Xeon ® @ 2.30GHz on Ubuntu 22.04 LTS 64-bit. The model was implemented in PyTorch [45] by building on top of PyTorch Geometric library (MIT license) [46] . PyTorch, NumPy [47] , SciPy [48] are BSD licensed, Matplotlib [49] is PSF licensed. 
experimental result: 
 The performances across different learning setups are summarized in Tab. 1. Results reflect the mean absolute error (MAE) and standard deviation across five initializations. The statistics are also computed across clients in isolated learning setups, while federated and centralized setups leverage collaborative training.The Isolated Learning approach, where models are trained independently for each client, shows weaker performance compared to collaborative methods. In the realistic isolated learning setup, a MAE of 3.68 ± 0.26 was obtained, while the idealized isolated setup, though more controlled, resulted in a higher MAE of 3.92 ± 0.87. The high variance in the idealized setting suggests that the data distribution across clients introduces inconsistencies in model performance, even when the data covers the full value range. This reflects the challenge of achieving stable results when training independently without data sharing.FL consistently outperformed isolated learning. In the realistic FL setup, FedAvg achieved a MAE of 3.23 ± 0.06, and SCAFFOLD slightly improved with an MAE of 3.22 ± 0.05. This indicates the robustness of federated learning in harnessing distributed data while maintaining privacy. The marginal improvement of SCAFFOLD over FedAvg in the realistic scenario suggests that SCAFFOLD's correction of client drift is particularly beneficial when client data distributions closely resemble real-world conditions.Performance was slightly lower in the Idealized Federated Learning setup, which uses a more balanced and mixed data distribution across clients. FedAvg reported MAE of 3.34 ± 0.08, while SCAFFOLD had an MAE of 3.44 ± 0.07. Although more evenly distributed, the idealized setup may obscure the natural variations in client data in realistic conditions. This could explain why models trained in the idealized setup generalize less effectively to the test set, which likely follows a more realistic data distribution. Thus, the realistic setup mirrors client-specific data patterns, enhancing predictive accuracy.The Centralized Learning setup, where all client data is pooled into a single model, achieved a MAE of 3.22 ± 0.12, which is identical to the performance of the Realistic Federated Learning setup using the SCAFFOLD algorithm. This highlights that federated learning, when properly configured, can achieve the same level of accuracy as centralized learning without requiring data sharing. Given the privacy constraints in medical settings, federated learning presents a substantial alternative, offering equivalent performance while preserving data locality and complying with privacy regulations. Fig. 4 provides further insights into model convergence across different setups. The plot shows that centralized learning achieves the fastest convergence, as expected, given its access to the full dataset. Among federated approaches, the realistic setup converges as smoothly as the idealized setup.Explainable Insights Integrating EdgeSHAPer values into our framework enables us to assign a Shapely value to each edge, which offers several key advantages: (a) Quantitative Edge Importance: Shapley values provide a theoretically robust measure of each edge's significance, enabling us to rank physiological connections based on their contributions to the model's predictions. This quantitative assessment ensures a fair and consistent evaluation of edge importance across brain networks.(b) Interpretable Visualizations: We visualize the brain network by coloring edges based on their corresponding Shapley values and sizing nodes according to their weighted degree centrality derived from those values (see Fig. 5 ). This dual representation effectively highlights critical brain regions and their interconnections, making the network dynamics more comprehensible.(c) Actionable Insights: Identifying the most significant edges allows clinicians to understand which neural pathways were most affected by the stroke. This understanding informs the development of targeted therapeutic interventions, enabling personalized treatment plans that address the specific neural disruptions identified by our model.We used a similarity metric based on the normalized Euclidean distance to assess the similarity between different model variants. This metric was applied to compare sets of weight vectors derived from the various experiments. Our analysis revealed an average similarity of 0.76, indicating high coherence among the weight vectors. Notably, the idealized subgroup exhibited an average similarity of 0.75, while the realistic subgroup achieved a higher similarity of 0.78. The similarity matrix demonstrated consistently high values across all vector pairs, ranging from 0.73 to 0.78. This approach provides insights into the convergence patterns and consistency of diverse federated learning strategies and setups. Furthermore, these results align with our observations regarding the best MAE, suggesting a strong correlation between higher performance metrics and more cohesive latent representations. Fig. 5 illustrates these findings, depicting the Edge Shapley Values for our federated learning setups and highlighting the relative contributions of different nodes and edges to the model's predictions.  
conclusions: 
 Broader Impact The proposed federated learning framework for stroke assessment can assist clinical neuroscientists' evaluations by enabling collaborative research and model development across multiple institutions while maintaining strict data privacy. By allowing hospitals to unify insights without sharing raw patient data, this approach addresses privacy concerns and broadens the scope of multi-healthcare collaborations. The model's ability to predict stroke severity with an error rate close to human performance and interpretability modules can help clinicians better understand brain network changes post-stroke, leading to more personalized treatment plans.Limitations While the proposed federated learning framework offers significant privacy advantages and demonstrates solid predictive performance, some limitations must be acknowledged. First, the relatively small sample size of 72 patients restricts the model's generalizability to more extensive and diverse clinical populations. Additionally, scalability to broader multi-institutional settings may face challenges due to variations in data quality, preprocessing protocols, and hardware capabilities across institutions. 
 
 Appendix: This study introduces a novel federated learning framework using GNNs for neurological assessments, demonstrating its ability to predict stroke severity from EEG data across multiple institutions. We show the effectiveness of collaborative model training while preserving data privacy. Incorporating explainability through EdgeSHAPer further enhances the model's potential clinical relevance by providing insights into the neural connections driving predictions. Future work will focus on expanding the dataset and addressing the technical challenges of scaling the system to larger, more diverse populations. 


Decision: In light of the positive reviewers' feedback and relevancy of the submission, we are pleased to accept this paper for presentation at UniReps 2024. We kindly ask the authors to incorporate and address the reviewers' suggestions and feedback in the final camera-ready version of the manuscript.

Reviews: review: title: title: Review in federated learning framework combined with GNNs
main_review: main_review: In this paper, the author proposes a federated learning framework combined with graph neural networks (GNNs) to predict stroke severity using EEG signals from distributed medical institutions. And the aim is to preserve patient data privacy while enabling hospitals to collaboratively train a shared GNN model. The approach leverages masked self-attention mechanisms to capture critical brain connectivity patterns and EdgeSHAP for post-hoc explainability of neurological states post-stroke. 

Pros: 

1. The combination of GNNs and Federated Learning for the EEG-based stroke assessment is novel. And I think that this proposed framework addresses privacy concerns.  2. I believe this paper conducted experiments across four institutions, which can be treated to strengthen the generalizability of the results. 3. Also, it includes strong emphasis on explainability with the inclusion of EdgeSHAP. This gives transparency.


Cons

1. The study highly bases on EEG data from 71 patients and I think we need to consider a larger and more diverse dataset even though we can derive the promising results.  2. The paper only evaluates through Mean Absolute Error (MAE) to stroke severity prediction. I believe including more metrics can provide more generalized evaluation of the model's performance. 3. The paper lacks the theoretical justification to GNN model, why we choose GATv2 rather than other GNN architectures? 4. The visualization in the paper of brain networks lack explanation to their roles in the paper. This would enhance paper's readability if the paper includes more how the visualization is related to stroke recovery and how they can be used in clinical practice. 


In summary, I would support this paper but there are lots of concerns need to be improved or further explanation.
title: title: Revision for Federated GNNs for EEG-Based Stroke Assessment
main_review: main_review: Thank you for all your nice work in Federated Learning!

The manuscript uses an existing Graph Neural Network, designed for stroke assessment on EEG data, together with Federated Learning paradigms to evaluate the predictions on different training cases. Maintaining the privacy of medical data is a very relevant task for patient security reasons and therefore the study is very important for the community. 

However, there are some issues, especially regarding the definition of the problem and the learning objective, that should be addressed. All issues are addressed below:

Introduction
The terms "EEG" and "MQTT protocol" are mentioned in the abstract (line 8) and the introduction (line 29) before being properly introduced.

The dataset description and processing details are misplaced in the introduction since the used datasets and preprocessing techniques are methodological choices.

Related Works
There are plenty of references to the study “Towards explainable graph neural networks for neurological evaluation on EEG signals.” (line 60), and the prediction model on EEG signals is based on it. However, the paper cannot be located and verified. Please ensure that all references are properly listed and accessible.

Experimental setup
The paper does not clearly specify how "stroke severity" is being quantified. It is unclear whether the metric used is based on the National Institutes of Health Stroke Scale (NIHSS) scores or another standard metric. There are several different ways of classifying a ‘minor stroke’ (e.g., https://doi.org/10.1161/STROKEAHA.109.572883), for example, and understanding the method used to define what you want to predict is essential for the reader’s understanding of the task. This should be clarified, and if some well-known metric was used, proper citation should be provided.

The paper does not clearly state what is the task of the prediction model, or what is the quantity being inferred. If it is a classification task, what would the classes be? Are them stroke severity levels based on a metric? If it is a regression task on quantities related to measuring stroke severity, would these quantities be?

Experimental results
Could you please explain better the choice of using Mean-Squared Error for assessing model performance, instead of Accuracy or Area Under The Curve (AUC), such as other papers in the area (e.g., https://iopscience.iop.org/article/10.1088/1742-6596/1528/1/012006, https://pubmed.ncbi.nlm.nih.gov/33116187/)?
title: title: The paper offers a well-written and structured study on predicting stroke severity using a novel Federated Learning approach with GNNs. The authors highlight the importance of data privacy in medical machine learning, enabling multiple institutions to train a shared GNN model on local EEG data without exchanging sensitive patient records.  A key strength is the clear explanation of the experimental setup, involving EEG data from four hospitals. The use of a masked self-attention mechanism and EdgeSHAP for model interpretation enhances both performance and explainability.  The results show that the Federated Learning method significantly improves prediction accuracy over isolated models, with a Mean Absolute Error of 3.23, close to the 3 achieved by human experts. However, the paper could benefit from including a relative measure, like percentage improvement, to better quantify the method’s performance gains over baselines.
main_review: main_review: The paper presents a well-structured study on predicting stroke severity using a novel Federated Learning approach with GNNs. The authors emphasize the importance of data privacy, allowing multiple institutions to collaboratively train a shared GNN model on local EEG data without exchanging patient records, thus preserving privacy while benefiting from pooled knowledge.

The experimental setup is clearly explained, detailing how Federated Learning was implemented across four hospitals. The model uses a masked self-attention mechanism to capture important brain connectivity patterns and EdgeSHAP for post-hoc explanations, enhancing both performance and interpretability.

The paper also includes a comparison between different setups, showing how the Federated Learning method outperforms models trained in isolation at each institution. The reported Mean Absolute Error of 3.23 is close to 3.0 achieved by human experts, highlighting the model’s clinical relevance.

While Mean Absolute Error is effective for evaluating performance, adding a relative measure like percentage improvement would provide a clearer view of the performance gains over baseline models.
title: title: Review
main_review: main_review: This work introduces a new framework combining Federated Learning (FL) and Graph Neural Networks (GNNs) to predict stroke severity using EEG data. The two topics are based on their respective motivations of privacy sensitiveness and structured learning. The proposed model allows distributed medical institutions to collaboratively train a GNN model on local EEG data without sharing sensitive patient information. The framework integrates explainability through EdgeSHAP and achieves competitive predictive accuracy in stroke severity assessment compared to human experts, all while maintaining data privacy across healthcare institutions.

Strengths:
- the use of federated learning is well motivated and the paper addresses privacy concerns in collaborating hospital using it. although this insight on fed learning is well known
- combining EEG data with GNNs for stroke assessment leverages brain network patterns for accurate predictions.
- the inclusion of EdgeSHAP enhances model explainability, providing insights into which neural connections most influence the predictions, which is crucial for clinical use.
- the framework is evaluated on real-world EEG datasets from multiple institutions.

Limitations:
- there is no rewiring strategy in GATv2 as indicated in line 86, or in the reference [28] to my best reading. the rewiring terminology could refer to sparsification instead. The use of the term 'rewiring' could be a misinterpretation of sparsification.
- the sentence in lines 95-96 'This process transforms each layer Gl = (V, El ) into a sparse graph G′l = (V, E′l ).. ' may be miswritten since 'a layer' is said to be 'transformed to a graph'. Or if it is indeed rewiring then perhaps the writing may appear confusing.
- equation 1 is not properly defined - there is no indication of what 'com' denotes. also additional terms are being introduced without substantial purpose such as h_{new} where 'new' may not be required and is also not defined if it is necessarily introduced.
- minor comment on fig 1 - the figure may not have been attributed appropriately.
- the focus of this work may not be contributing enough on 'unifying representations' despite the paper mentioning this at lines 61-62. the purpose of fed learning may not be unifying representations but instead allow collaborative learning or privacy preserving learning across data or server nodes.