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REVIEW 4 major objections 6 minor 45 references

Federated Graph Learning with Graphless Clients

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read FedGLS transfers graph-structure knowledge from clients with edges to graphless clients through a distilled feature encoder, letting feature-only clients learn usable local graphs and approach full-graph accuracy.

desk verdict Fresh problem setting, clean method, decent results—but the claimed structure-transfer channel is not isolated, and the experiments lack significance tests. read the letter →

arxiv 2411.08374 v1 pith:5QNTELWU submitted 2024-11-13 cs.LG cs.DC

Xingbo Fu , Song Wang , Yushun Dong , Binchi Zhang , Chen Chen , Jundong Li This is my paper · ORCID
classification cs.LGcs.DC
keywords federatedlearninggraphneuralnetworksgraphlessclientsstructureknowledgedistillationcontrastivenodeclassification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Federated graph learning usually assumes every client has both node features and edge structure. FedGLS removes that assumption: it introduces 'graphless clients' that own only node features and shows they can still train effective GNNs by learning local graph structure from structure knowledge transferred by the other clients. The transfer works by giving every client a feature encoder (an MLP) that imitates its GNN's node embeddings through knowledge distillation, then federated-averaging both the GNN and the encoder parameters; the averaged encoder carries structure knowledge to graphless clients, where a local graph learner generates an adjacency matrix and is trained by a contrastive loss that aligns the GNN and encoder embeddings. Across five benchmark datasets, FedGLS consistently beats five baselines, including kNN-based structure construction, and approaches the accuracy of a system that uses the real graph structures.

What carries the argument

The load-bearing object is the feature encoder $h(\cdot; \phi)$, a feature-only MLP that is trained to match the GNN's node embeddings through the knowledge-distillation loss $\mathcal{L}_{KD}^{(k)}$ of Eq. (11). Together with the GNN parameters $\theta$, the encoder parameters $\phi$ are aggregated on the server by the FedAvg-style update of Eq. (12), so the globally averaged encoder acts as the transport vehicle for structure knowledge. On each graphless client this averaged encoder supervises a graph learner $g(\cdot; \omega)$ that generates an adjacency matrix $S^{(k)}$ via an attentive encoder followed by a kNN-based adjacency processor; the graph learner is trained by the NT-Xent contrastive loss $\mathcal{L}_{CL}^{(k)}$ of Eq. (9), which pulls together the GNN embeddings $Z^{(k)}$ and encoder embeddings $H^{(k)}$ for the same node and pushes apart embeddings of different nodes.

What would settle it

Remove the knowledge-distillation loss from the feature encoder (or randomize the encoder before aggregation) and measure graphless-client accuracy: if it does not fall toward the feature-only baseline, the structure-transfer mechanism is not what drives FedGLS's gain. A complementary check is to hold out the true edge sets of graphless clients and test whether the adjacency matrices learned by FedGLS preserve the label-predictive edges.

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Extended reading notes

Core claim

The central claim is that graph-structure knowledge in a federated system can be moved from clients that have edges to clients that have none, using a feature encoder as a carrier. On each client the GNN produces node embeddings over the local graph, and an MLP feature encoder is distilled to reproduce those embeddings from features alone. The server averages both the GNN parameters and the encoder parameters with FedAvg, so the aggregated encoder becomes a global summary of structure-aware behavior. On a graphless client, a graph learner generates a sparse symmetric adjacency matrix from node features and is trained with a contrastive loss that maximizes agreement between the GNN embeddings computed on that generated graph and the encoder embeddings; this gives the graph learner an external structure signal to imitate. The result is that graphless clients learn usable adjacency matrices without ever seeing an edge, and the joint model's node-classification accuracy approaches that of training with true graph structure.

Load-bearing premise

The framework assumes that averaging the feature-encoder parameters across clients transfers usable graph-structure knowledge, and that a feature encoder trained by knowledge distillation preserves enough of that structure for a graphless client's graph learner to learn from; if the structure signal is lost in distillation or averaging, the contrastive objective has no useful teacher and FedGLS degenerates into feature-only training.

Editorial extensions

If this is right

  • Graphless clients can train GNNs without ever observing edges, as long as other clients share structure knowledge through the federated aggregation of encoder and GNN parameters.
  • FedGLS outperforms kNN-based structure construction (Fed-GNNk) and feature-only baselines on all five datasets, with accuracy approaching that of Fed-GNN, which uses the true graph structures.
  • FedGLS converges faster than the best baseline Fed-GNNk on Cora and Flickr.
  • The framework's performance is stable across local epoch choices and graphless-client ratios, degrading gracefully as the fraction of graphless clients grows.
  • FedGLS adds only a small computational overhead on clients because the feature encoder is an MLP whose cost is much smaller than the GCN's.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same distillation-aggregation recipe could apply to other missing-data settings, for example clients that lack node features but have edges, by swapping which modality is distilled.
  • The contrastive objective that pulls GNN embeddings toward the encoder also discourages over-smoothing, so the accuracy gain on graphless clients may come partly from more stable GNN training rather than from faithful structure recovery; the paper's fixed-k sparsification suggests the learned structures are approximate.
  • A direct testable extension would be to use FedGLS's learned adjacency matrices as a warm start for structure refinement on graphless clients, or to measure how the fidelity of the transferred structure varies with the fraction of graphless clients.
  • The paper's fixed-k neighbor sparsification bounds the expressiveness of learned graphs; allowing the sparsity pattern to vary per node or be learned could improve recovery of underlying structure.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper introduces FedGLS, a federated graph learning framework for settings where some clients have complete graph data (node features plus edges) while other 'graphless' clients have only node features. Each client trains a GNN and a feature encoder; on graphless clients, a learnable graph learner generates a local adjacency matrix, and the feature encoder is trained by knowledge distillation from the GNN. Structure knowledge is claimed to be transferred across clients by FedAvg aggregation of both the GNN parameters and the feature-encoder parameters. The graph learner is optimized with a contrastive loss that encourages the GNN embeddings on the generated graph to agree with feature-encoder embeddings. Experiments on Cora, CiteSeer, PubMed, Flickr, and ogbn-arxiv compare FedGLS against five baselines and an oracle that uses true graph structures, with sensitivity studies over local epochs and graphless-client ratios. The paper reports that FedGLS consistently achieves the highest accuracy among the five baselines and converges faster than Fed-GNNk, the best baseline.

Significance. If the structure-transfer mechanism is sound, the paper addresses a novel and practically relevant gap: many federated systems have clients that lack explicit edge information, and the proposed pipeline offers a way for such clients to benefit from structure knowledge held by other clients. The formal problem definition, the clear algorithmic description, and the breadth of experiments across five datasets are strengths, as are the sensitivity studies over local epochs and graphless-client ratios. The manuscript also states that an implementation is available. However, the central claim that graphless clients learn structure from other clients’ knowledge is not isolated experimentally, and the distillation loop in Eqs. (9)–(11) creates a self-referential training signal that could allow the graph learner to succeed without any cross-client structure transfer. Because the paper's contribution is precisely that transfer channel, the current evidence falls short of establishing the mechanism, even if the reported accuracy numbers are reproducible.

major comments (4)
  1. [Sec. 4.2, Eq. (11); Algorithm 1] The claimed structure-transfer mechanism is not isolated. On every graphless client, phi is updated by the KD loss in Eq. (11), where the teacher is the local GNN whose input adjacency S is produced by the graph learner itself. After the first round, the global phi aggregated in Eq. (12) therefore contains a self-referential component from graphless clients, and the H used as the target in the contrastive loss Eq. (9) is not an independent cross-client teacher. The paper reports no ablation that removes or varies this transfer channel, such as freezing phi on graphless clients, excluding graphless-client phi from aggregation, or using a teacher derived only from structured clients. Without such an ablation, the accuracy gains over Fed-GNNk in Table 1 could be produced by the graph learner fitting local label structure through the CE-trained GNN (Eq. 10), rather than by structure knowledge transferred from other clients. This is load-bearing for the paper's central claim.
  2. [Sec. 5.1.2, Table 1] The baseline set is too weak to support the claim that federated structure transfer is the source of the gains. Fed-GNNk and Local-GNNk use fixed kNN graphs, which are known to be poorly suited to node classification. There is no baseline that applies a learnable graph-structure-learning method (e.g., an adapted IDGL or SL-style learner) to graphless clients, and no ablation in which the graph learner is trained without any cross-client aggregation. Such a comparison is needed to distinguish the benefit of transferred structure knowledge from the benefit of adaptively learning a task-specific graph from local features and labels.
  3. [Table 1] No statistical significance tests are reported, despite several small margins relative to the reported standard deviations. For example, on PubMed FedGLS reports 0.8491±0.0070 versus Fed-GNNk's 0.8426±0.0128, and on CiteSeer FedGLS reports 0.8058±0.0171 versus FedProto's 0.7876±0.0184. The claim that FedGLS 'consistently achieves the highest classification accuracy' requires paired significance tests or confidence intervals over the five repetitions, especially because the differences are not uniformly large.
  4. [Sec. 5.2.2, Fig. 3] The convergence-speed claim is supported only on Cora and Flickr, and the curves in the submitted text are not readable. Please report convergence curves for all five datasets, or justify why the two selected datasets are representative, and state clearly whether the plotted loss is the average local training loss or the global objective. This matters because the claim that the adaptive graph learner accelerates convergence is part of the paper's experimental contribution.
minor comments (6)
  1. [Eq. (8)] The denominator in Eq. (8) is written ambiguously: the summation upper limit should be explicitly n(k), and the bracket placement after 1[i≠j] should be clarified to show that both exponentials are inside the sum.
  2. [Sec. 4.2, Eq. (11)] The KL divergence uses f(z_i; theta_c) and f(h_i; theta_c), where theta_c is the shared classifier head, but the paper does not explicitly state that theta_c is updated jointly with the GNN encoder via Eq. (10) and is also part of the aggregated theta in Eq. (12). Please clarify the role and update rule of theta_c.
  3. [Appendix C] The complexity analysis covers only the GCN and MLP forward/backward costs. It omits the graph learner, the adjacency generation, and the kNN sparsification cost, so the conclusion that the feature encoder adds negligible cost is not a complete comparison of FedGLS against Fed-GNNk.
  4. [Sec. 5.1.2, Table 1] The Fed-GNN column is listed in Table 1 but is not described as a baseline in the Experimental Setup section. Please state explicitly that it is an oracle or upper-bound method using true graph structures on graphless clients.
  5. [Algorithm 1] The graph learner is updated only once per round, before the E local epochs for theta and phi, while theta and phi are updated E times. This asymmetry is not discussed, and the sensitivity study only varies E; the effect of the number of graph-learner updates per round is not explored.
  6. [Sec. 3.2, Problem 1] The notation C1 = {c(k)}_{k=1}^{M} and C2 = {c(k)}_{k=M+1}^{K} is introduced but not used consistently in later sections; for example, Sec. 4.4 refers to 'clients in C1' without restating the range of k. A table or explicit restatement of the two client sets would improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the global feature-encoder aggregation supplies an external cross-client structure signal, and the local distillation loop is an approximate self-consistency training mechanism, not an identity.

full rationale

FedGLS does not reduce its reported gains to its own inputs by construction. On a graphless client, the graph learner is updated by Eq. (9) using embeddings Z(k)=f(X(k),S(k);theta(k)) and H(k)=h(X(k);phi(k)), where phi(k) is initialized from the server-broadcast global phi. In Algorithm 1 the omega update is performed before the local phi update, so within a round the graph learner's target is the global feature encoder, not a copy of the just-updated local GNN output. The global phi from Eq. (12) aggregates parameters from structured clients whose feature encoders were distilled from GNNs operating on real graphs, so external structure knowledge can enter the loop. Across rounds, the averaged phi contains both external and self-referential components, but no equation makes H identically equal to Z: the KL and contrastive losses are approximate training objectives, not definitions. The headline claims are held-out node-classification accuracies in Table 1, compared against five baselines and a real-graph upper bound; no fitted parameter is relabeled as a prediction. The self-citations (Fu et al. 2022, 2023, 2024; Wang et al. 2023, 2024b) appear in related-work and motivation, while the load-bearing distillation precedent (Hinton et al. 2015; Zhang et al. 2022) and graph-generator components (Liu et al. 2022) are external. The Conclusion's admitted limitation that generated structures may not match unknown real structures is a correctness risk, not a circularity. The absence of an ablation isolating the transfer channel is an empirical weakness, but it does not make the central claim equivalent to its inputs by construction.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard machine learning assumptions (distillation, contrastive learning, FedAvg) plus the domain-specific premise that structure knowledge can be distilled into an MLP and transferred via parameter averaging. No new physical or mathematical entities are postulated; the graph learner is a standard neural module. Hyperparameters such as temperature, learning rates, hidden size, and sparsification k are chosen by hand without extensive tuning analysis.

free parameters (5)
  • Temperature parameter tau in contrastive loss (Eq. 8) = 0.2
    Chosen by hand; controls the sharpness of the NT-Xent distribution and affects graph learner training.
  • Graph learner learning rate gamma = 0.001
    Set separately from the GNN and encoder rates; affects how quickly the generated adjacency adapts.
  • Hidden size of GNN and feature encoder = 16
    Fixed across models and datasets; no sensitivity study reported for this choice.
  • Sparsification parameter k in the adjacency processor (Eq. 16) = not reported
    Fixed number of neighbors per node in the generated graph; the paper's own limitation section notes this may not match real structures.
  • Local epochs E = 5 (varied 3 and 10 in sensitivity)
    Standard hyperparameter; sensitivity is shown but only on two datasets and without significance testing.
assumptions (5)
  • domain assumption Knowledge distillation from a GNN to an MLP feature encoder preserves sufficient graph-structure information for downstream transfer.
    Core to FedGLS; relies on GLNN results (Zhang et al., 2022) and is not proven in the federated setting.
  • domain assumption FedAvg aggregation of feature-encoder parameters phi transfers structure knowledge across clients.
    The paper assumes averaging phi in Eq. (12) shares structure knowledge; no convergence or information-theoretic analysis is provided.
  • domain assumption Maximizing consistency between Z and H via contrastive loss (Eq. 9) yields useful graph structures rather than trivial collapse.
    The graph learner relies on this; the risk of collapse is mentioned only indirectly via the over-smoothing comment and is not analyzed.
  • domain assumption Louvain community splitting produces realistic federated graph data distributions.
    All experiments use synthesized federated datasets; the realism of this partitioning is assumed from prior work (Huang et al., 2023; Zhang et al., 2021b).
  • ad hoc to paper The shared classifier head theta_c used in the KL distillation (Eq. 11) provides a valid teacher signal for the feature encoder.
    The feature encoder is trained against the GNN's predictor outputs, coupling encoder training to the classifier; this is a design choice specific to FedGLS.

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Cite this review

Pith. "Pith review of Federated Graph Learning with Graphless Clients." pith.science (2026). https://pith.science/paper/5QNTELWU

@misc{pith2026241108374,
  author       = {Pith},
  title        = {Pith review of: Federated Graph Learning with Graphless Clients},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5QNTELWU}},
  note         = {Machine review of arXiv:2411.08374}
}
read the original abstract

Federated Graph Learning (FGL) is tasked with training machine learning models, such as Graph Neural Networks (GNNs), for multiple clients, each with its own graph data. Existing methods usually assume that each client has both node features and graph structure of its graph data. In real-world scenarios, however, there exist federated systems where only a part of the clients have such data while other clients (i.e. graphless clients) may only have node features. This naturally leads to a novel problem in FGL: how to jointly train a model over distributed graph data with graphless clients? In this paper, we propose a novel framework FedGLS to tackle the problem in FGL with graphless clients. In FedGLS, we devise a local graph learner on each graphless client which learns the local graph structure with the structure knowledge transferred from other clients. To enable structure knowledge transfer, we design a GNN model and a feature encoder on each client. During local training, the feature encoder retains the local graph structure knowledge together with the GNN model via knowledge distillation, and the structure knowledge is transferred among clients in global update. Our extensive experiments demonstrate the superiority of the proposed FedGLS over five baselines.

Figures

Figures reproduced from arXiv: 2411.08374 by the authors.

Figure 1
Figure 1. An example of a healthcare system including four hospitals. In this example, Hospital A and [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. An overview of the proposed FedGLS. GNN model. It approximates the output (i.e., node embeddings) of the GNN model using the knowledge learned by the GNN model. On each graphless client, a graph learner generates local graph structure and learns the structure knowledge via a contrastive loss. Finally, the well-trained graph learner produces local graph structure and the GNN model learns more expressive node embeddin… view at source ↗
Figure 3
Figure 3. Result for convergence speeds of FedGLS and Fed-GNNk: (a) training loss curve and (b) test [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗

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Reference graph

Works this paper leans on

45 extracted references · 37 canonical work pages

  1. [1]

    Fast unfolding of communities in large networks

    Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008

    work page 2008

  2. [2]

    A simple framework for contrastive learning of visual representations

    Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, 2020 a

    2020

  3. [3]

    Iterative deep graph learning for graph neural networks: Better and robust node embeddings

    Yu Chen, Lingfei Wu, and Mohammed Zaki. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. In Advances in neural information processing systems, 2020 b

    work page 2020

  4. [4]

    Learning discrete structures for graph neural networks

    Luca Franceschi, Mathias Niepert, Massimiliano Pontil, and Xiao He. Learning discrete structures for graph neural networks. In ICML, 2019

    work page 2019

  5. [5]

    Federated graph machine learning: A survey of concepts, techniques, and applications

    Xingbo Fu, Binchi Zhang, Yushun Dong, Chen Chen, and Jundong Li. Federated graph machine learning: A survey of concepts, techniques, and applications. ACM SIGKDD Explorations Newsletter, 2022

    work page 2022

  6. [6]

    Spatial-temporal networks for antibiogram pattern prediction

    Xingbo Fu, Chen Chen, Yushun Dong, Anil Vullikanti, Eili Klein, Gregory Madden, and Jundong Li. Spatial-temporal networks for antibiogram pattern prediction. In 2023 IEEE 11th International Conference on Healthcare Informatics, 2023

    work page 2023

  7. [7]

    Federated graph learning with structure proxy alignment

    Xingbo Fu, Zihan Chen, Binchi Zhang, Chen Chen, and Jundong Li. Federated graph learning with structure proxy alignment. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024

    work page 2024

  8. [8]

    Generating classification weights with gnn denoising autoencoders for few-shot learning

    Spyros Gidaris and Nikos Komodakis. Generating classification weights with gnn denoising autoencoders for few-shot learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019

    work page 2019

Show all 45 references
  1. [9]

    A variational edge partition model for supervised graph representation learning

    Yilin He, Chaojie Wang, Hao Zhang, Bo Chen, and Mingyuan Zhou. A variational edge partition model for supervised graph representation learning. Advances in Neural Information Processing Systems, 2022

    2022

  2. [10]

    Distilling the knowledge in a neural network

    Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015

    2015 arXiv

  3. [11]

    Open graph benchmark: Datasets for machine learning on graphs

    Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. 2020

    2020

  4. [12]

    Federated graph semantic and structural learning

    Wenke Huang, Guancheng Wan, Mang Ye, and Bo Du. Federated graph semantic and structural learning. In Proceedings of the 32nd International Joint Conference on Artificial Intelligence, 2023

    2023

  5. [13]

    Graph structure learning for robust graph neural networks

    Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. Graph structure learning for robust graph neural networks. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2020

    2020

  6. [14]

    Scaffold: Stochastic controlled averaging for federated learning

    Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, 2020

    2020

  7. [15]

    Adam: A method for stochastic optimization

    Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations, 2015

    2015

  8. [16]

    Model-contrastive federated learning

    Qinbin Li, Bingsheng He, and Dawn Song. Model-contrastive federated learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021

    2021

  9. [17]

    Federated learning: Challenges, methods, and future directions

    Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Federated learning: Challenges, methods, and future directions. IEEE signal processing magazine, 2020 a

    2020

  10. [18]

    Federated optimization in heterogeneous networks

    Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimization in heterogeneous networks. In Proceedings of Machine learning and systems, 2020 b

    2020

  11. [19]

    Towards unsupervised deep graph structure learning

    Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, and Shirui Pan. Towards unsupervised deep graph structure learning. In Proceedings of the ACM Web Conference, 2022

    2022

  12. [20]

    Communication-efficient learning of deep networks from decentralized data

    Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics, 2017

    2017

  13. [21]

    Fedproc: Prototypical contrastive federated learning on non-iid data

    Xutong Mu, Yulong Shen, Ke Cheng, Xueli Geng, Jiaxuan Fu, Tao Zhang, and Zhiwei Zhang. Fedproc: Prototypical contrastive federated learning on non-iid data. Future Generation Computer Systems, 2023

    2023

  14. [22]

    Differentially private federated knowledge graphs embedding

    Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, and Jianxin Li. Differentially private federated knowledge graphs embedding. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021

    2021

  15. [23]

    Collective classification in network data

    Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI magazine, 2008

    2008

  16. [24]

    Personalized federated learning using hypernetworks

    Aviv Shamsian, Aviv Navon, Ethan Fetaya, and Gal Chechik. Personalized federated learning using hypernetworks. In International Conference on Machine Learning, 2021

    2021

  17. [25]

    Bring your own view: Graph neural networks for link prediction with personalized subgraph selection

    Qiaoyu Tan, Xin Zhang, Ninghao Liu, Daochen Zha, Li Li, Rui Chen, Soo-Hyun Choi, and Xia Hu. Bring your own view: Graph neural networks for link prediction with personalized subgraph selection. In Proceedings of the Sixteenth ACM International Conference on Web Search and Data...

    2023

  18. [26]

    Fedproto: Federated prototype learning across heterogeneous clients

    Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, and Chengqi Zhang. Fedproto: Federated prototype learning across heterogeneous clients. In Proceedings of the AAAI conference on artificial intelligence, 2022

    2022

  19. [27]

    Federated learning on non-iid graphs via structural knowledge sharing

    Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. Federated learning on non-iid graphs via structural knowledge sharing. In Proceedings of the AAAI conference on artificial intelligence, 2023 b

    2023

  20. [28]

    The eu general data protection regulation (gdpr)

    Paul Voigt and Axel Von dem Bussche. The eu general data protection regulation (gdpr). A Practical Guide, 1st Ed., Cham: Springer International Publishing, 2017

    2017

  21. [29]

    Rethinking personalized federated learning with clustering-based dynamic graph propagation

    Jiaqi Wang, Yuzhong Chen, Yuhang Wu, Mahashweta Das, Hao Yang, and Fenglong Ma. Rethinking personalized federated learning with clustering-based dynamic graph propagation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2024 a

    2024

  22. [30]

    Federated few-shot learning

    Song Wang, Xingbo Fu, Kaize Ding, Chen Chen, Huiyuan Chen, and Jundong Li. Federated few-shot learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

    2023

  23. [31]

    Safety in graph machine learning: Threats and safeguards

    Song Wang, Yushun Dong, Binchi Zhang, Zihan Chen, Xingbo Fu, Yinhan He, Cong Shen, Chuxu Zhang, Nitesh V Chawla, and Jundong Li. Safety in graph machine learning: Threats and safeguards. arXiv preprint arXiv:2405.11034, 2024 b

    2024 arXiv

  24. [32]

    Heterogeneity-aware memory efficient federated learning via progressive layer freezing

    Yebo Wu, Li Li, Chunlin Tian, Tao Chang, Chi Lin, Cong Wang, and Cheng-Zhong Xu. Heterogeneity-aware memory efficient federated learning via progressive layer freezing. In 2024 IEEE/ACM 32nd International Symposium on Quality of Service (IWQoS), 2024 a

    2024

  25. [33]

    Breaking the memory wall for heterogeneous federated learning with progressive training

    Yebo Wu, Li Li, Chunlin Tian, and Chengzhong Xu. Breaking the memory wall for heterogeneous federated learning with progressive training. arXiv preprint arXiv:2404.13349, 2024 b

    2024 arXiv

  26. [34]

    A comprehensive survey on graph neural networks

    Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2020

    2020

  27. [35]

    Federated graph classification over non-iid graphs

    Han Xie, Jing Ma, Li Xiong, and Carl Yang. Federated graph classification over non-iid graphs. In Advances in neural information processing systems, 2021

    2021

  28. [36]

    Federated node classification over graphs with latent link-type heterogeneity

    Han Xie, Li Xiong, and Carl Yang. Federated node classification over graphs with latent link-type heterogeneity. In Proceedings of the ACM Web Conference, 2023

    2023

  29. [37]

    Federated machine learning: Concept and applications

    Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated machine learning: Concept and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 2019

    2019

  30. [38]

    Graphsaint: Graph sampling based inductive learning method

    Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. Graphsaint: Graph sampling based inductive learning method. In International Conference on Learning Representations, 2020

    2020

  31. [39]

    Ppsgcn: A privacy-preserving subgraph sampling based distributed gcn training method

    Binchi Zhang, Minnan Luo, Shangbin Feng, Ziqi Liu, Jun Zhou, and Qinghua Zheng. Ppsgcn: A privacy-preserving subgraph sampling based distributed gcn training method. arXiv preprint arXiv:2110.12906, 2021 a

    2021 arXiv

  32. [40]

    Subgraph federated learning with missing neighbor generation

    Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, and Siu Ming Yiu. Subgraph federated learning with missing neighbor generation. In Advances in neural information processing systems, 2021 b

    2021

  33. [41]

    Link prediction based on graph neural networks

    Muhan Zhang and Yixin Chen. Link prediction based on graph neural networks. Advances in neural information processing systems, 2018

    2018

  34. [42]

    Graph-less neural networks: Teaching old mlps new tricks via distillation

    Shichang Zhang, Yozen Liu, Yizhou Sun, and Neil Shah. Graph-less neural networks: Teaching old mlps new tricks via distillation. In International Conference on Learning Representations, 2022

    2022

  35. [43]

    Vertically federated graph neural network for privacy-preserving node classification

    Jun Zhou, Chaochao Chen, Longfei Zheng, Huiwen Wu, Jia Wu, Xiaolin Zheng, Bingzhe Wu, Ziqi Liu, and Li Wang. Vertically federated graph neural network for privacy-preserving node classification. In Proceedings of the 31st International Joint Conference on Artificial Intelligence, 2022

    2022

  36. [44]

    Deep graph structure learning for robust representations: A survey

    Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu, and Liang Wang. Deep graph structure learning for robust representations: A survey. arXiv preprint arXiv:2103.03036, 2021

    2021 arXiv

  37. [45]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.