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REVIEW 3 major objections 5 minor 59 references

GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read GENE-FL compresses client updates into ~9 MB learnGene fragments, cutting communication about 4x versus FedAvg while sustaining accuracy on SVHN, CIFAR-10, and CIFAR-100 and giving new agnostic clients a fast, effective starting model.

desk verdict A promising Learngene-based FL framework whose headline 4x communication saving depends on an uplink-only accounting that the protocol's full-model downloads contradict. read the letter →

arxiv 2504.14628 v1 pith:MUOMERZ6 submitted 2025-04-20 cs.DC cs.LG

Shunxin Guo , Jiaqi Lv , Qiufeng Wang , Xin Geng This is my paper · ORCID
classification cs.DCcs.LG
keywords federatedlearningdynamicagnosticclientsLearngeneparameter-efficientcommunicationFisherinformationmodelinitializationnon-iiddatalearnGenecondensation
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

This paper argues that federated learning can interact through small, masked parameter fragments called learnGenes rather than full models, and that these fragments are enough to initialize brand-new clients whose label sets were never seen during training. The claim is that per-client condensation, per-cluster aggregation, and fragment-based initialization cut upload traffic by about 4x compared with FedAvg on SVHN, CIFAR-10, and CIFAR-100, under both sharding and Dirichlet non-iid partitions, while preserving or improving accuracy. A dynamically joining agnostic client can start from roughly 9.04 MB of inherited parameters instead of the full model, and the experiments report faster convergence and higher final accuracy than baselines that initialize with larger or complete models. The paper's central value is showing that a statistically selected subset of parameters can carry the cross-task knowledge needed for rapid adaptation in dynamic federated settings.

What carries the argument

The load-bearing object is the learnGene: a masked subnetwork made of the top $\gamma$ layers of a client model, chosen by the layer-wise score $\xi^{(l)}_{k,i} = \cos(\theta^{(l)}_{k,i}, \tilde\theta^{(l)}_{k,i}) / \dim(\theta^{(l)}_{k,i})$, normalized to sum to one. The Fisher diagonal computed on local data decides which individual parameters are elastic: low-Fisher parameters are restored to the cluster model while high-Fisher parameters are allowed to move, and the quadratic loss $L_{\mathrm{elg}}$ enforces this split. The framework's three coupled operations are condensation (smooth local updates plus Fisher-constrained regularization), server-side aggregation of shared learnGene layers, and truncated-SVD subspace-distance matching that routes each new client to the nearest cluster learnGene. This machinery carries the argument because it turns communication and initialization of full models into exchanging only statistically selected fragments.

What would settle it

Take a trained model and a copy of it, perturb a single known layer by a fixed amount, and compute $\xi^{(l)}$ for the perturbed layer and an untouched layer; if the untouched layer scores higher, the formula behaves as cosine similarity predicts. Then run two variants of GENE-FL on CIFAR-10 with s=4, one selecting the top $\gamma$ layers by $\xi$ and the other selecting the bottom $\gamma$ layers; if the bottom-selecting variant matches or beats the top-selecting variant, the claimed selection mechanism is not what drives the results.

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Extended reading notes

Core claim

GENE-FL's central claim is that the information needed to initialize an agnostic client can be condensed into a per-cluster learnGene of about 9.04 MB without sacrificing accuracy. Locally, each client first moves toward its cluster model under an L2 smoothness penalty $L_{\mathrm{gen}}$, then applies a Fisher-information-weighted quadratic constraint $L_{\mathrm{elg}}$ that keeps low-Fisher parameters tied to the cluster's elastic learnGene while letting high-Fisher parameters specialize. The client scores every layer by cosine similarity between the updated and previous model, masks all but the top $\gamma$ layers, and uploads only those masked parameters. The server averages the shared layers from clients in the same cluster to form the cluster learnGene, and a joining agnostic client receives the nearest cluster's learnGene, initializing its model as $\theta_{k,i} = [\theta_0; \Theta_k^G]$. The reported effect is that uploading only these fragments costs about one quarter of FedAvg's full-model communication, and the inherited fragment reaches or exceeds the accuracy of larger initialized models in most tested configurations.

Load-bearing premise

The method depends on the layer score $\xi$ computed as cosine similarity between the updated and previous local model to separate personalized layers from generalizable layers; the paper treats high $\xi$ as personalized and low $\xi$ as generalizable, but for cosine similarity a high value means the layer changed little, so the selection rule may be inverted.

Editorial extensions

If this is right

  • Uploading learnGene fragments instead of full models cuts client-to-server communication by roughly 4x compared with FedAvg on the tested datasets and partitions.
  • Agnostic clients with label sets disjoint from the known clients can be initialized from a roughly 9.04 MB cluster learnGene and still reach or exceed the accuracy of clients initialized with full cluster models.
  • Communication cost declines over training rounds as the learnGene stabilizes, while FedAvg's per-round cost stays constant.
  • Inheriting fewer, more generalizable parameters can beat inheriting a complete pretrained model, because full-model initialization overfits known classes and adapts less to new class distributions.
  • Both loss components $L_{\mathrm{gen}}$ and $L_{\mathrm{elg}}$ contribute to the reported accuracy, and removing either one lowers performance in the ablation study.

Reading between the lines

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

  • If the Fisher-based elastic constraint is what preserves generalization, then the learnGene could plausibly be compressed further by switching from a layer-level mask to a parameter-level mask, since the Fisher diagonal already supplies per-parameter importance; the paper does not test this variant.
  • Because the layer score $\xi$ uses cosine similarity, a high value means the layer barely moved, so the paper's stated interpretation that high $\xi$ marks personalized layers appears inverted; if the mask is actually selecting the least-changed layers, the method might still work, but for the opposite stated reason.
  • The truncated-SVD subspace-distance selection suggests a testable extension to settings where clients and the server use different model architectures, with learnGenes matched by subspace similarity rather than by shared layer indices.
  • The privacy evaluation relies on a single iDLG-style gradient-inversion attack; a broader privacy claim would require testing under stronger reconstruction attacks or explicit differential-privacy guarantees.
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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

3 major / 5 minor

Summary. GENE-FL addresses dynamic agnostic federated learning (DAFL) by replacing full model exchanges with small 'learnGene' fragments. It clusters known clients into K groups, applies Fisher-information-based quadratic constraints to local updates, selects layers via a cosine-similarity sensitivity score, aggregates the selected layers at the server, and uses the resulting cluster learnGene to initialize agnostic clients. The paper reports roughly 4x lower communication cost than FedAvg, about 9.04 MB initialization, and accuracy gains on SVHN, CIFAR-10, and CIFAR-100 under sharding and Dirichlet partitions, plus a privacy study with iDLG.

Significance. The learnGene-in-FL idea is timely and the empirical sweep is broad. The paper includes ablations of the two loss components, hyperparameter studies, a clustering analysis, and PSNR-based privacy comparisons. If the communication accounting is corrected and the layer-selection rule is disambiguated, the proposed framework is a useful addition to personalization and initialization in federated learning. However, the two load-bearing issues below currently prevent accepting the central claims as stated.

major comments (3)
  1. [Section IV-B, Algorithm 1, Algorithm 2] The headline '4x communication cost reduction' is an uplink-only figure and is not supported as a full communication-cost claim. Algorithm 1 line 4 requires each participating client to 'Receive Θk from server' and Algorithm 2 line 7 sends the full updated cluster model Θk to participating clients every round. Since Eq. (4), Eq. (6), and Eq. (8) depend on the current Θk, a client cannot operate with only the small learnGene. With ResNet18 the full model download is about 42 MB per client per round, so the per-round per-client traffic is roughly 9 MB upload plus 42 MB download, about 51 MB, against FedAvg's 42 + 42 = 84 MB; this is about 1.6x, not 4x. The abstract and Section IV-B do not restrict the claim to uplink traffic, so the central advertised advantage is overstated.
  2. [Section III-A, Eq. (9), Algorithm 1 lines 11-13] The text and the mask direction are mutually contradictory. The paper states that higher ξ(l) indicates stronger influence of private data (personalization) and lower ξ(l) indicates generalized information, yet Algorithm 1 sorts ξ in descending order and selects the top γ layers as the learnGene. For the cosine similarity in Eq. (9), a larger similarity between θ and the previous model θ̃ means a smaller update, i.e., a more stable or general layer, so the selection should be reversed if the learnGene is meant to carry generalizable knowledge. Either Eq. (9) is not the intended measure or the mask direction is inverted; as written, the method may upload the most personalized layers, contradicting its own rationale.
  3. [Section IV-B, definition of Comm] The formula Comm = R × B × |W| × 2 does not reproduce the values in Tables I and II. For FedAvg with ResNet18, 100 rounds, 10 participating clients per round, and a 42.66 MB model, uploads are about 42.66 GB, matching the tables only if the formula includes the number of participating clients; the stated formula, which contains no number of clients, yields roughly 8.9 GB under float32. The '×2' is also unexplained for an explicitly upload-only metric. Please provide a corrected formula and state clearly whether the 4x comparison is uplink-only or bidirectional.
minor comments (5)
  1. [Section IV-B, first paragraph] The text says 'Table II presents ... Sharding strategy', but the Sharding results are in Table I; Table II reports the DDA results.
  2. [Section II and Tables I-IV] The FEDFINA baseline is used repeatedly but is never defined or cited in the related work or reference list; please add a citation and a one-sentence description.
  3. [Section III-A, Eq. (5)] The likelihood h(θ|D_i) is not defined; please state the distributional assumption used to compute the diagonal Fisher values.
  4. [Section IV-A, Table VI] The hyperparameter study varies K, ε, λ1, and λ2 but not γ or the SVD dimension d; since γ directly determines the learnGene size and communication cost, a sensitivity study for γ would strengthen the communication claims.
  5. [Algorithms 1-2] Algorithm 2 line 7 sends the full cluster model Θk to participating clients while line 10 sends only the cluster learnGene ΘGk to agnostic clients; the asymmetry should be explained explicitly in the system model.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: accuracy and initialization claims are benchmark-validated; the 4x communication claim is an accounting/metric issue, not a circular derivation.

full rationale

GENE-FL's derivation chain is self-contained. The local objective (Lall = Lcls + lambda1*Lgen + lambda2*Lelg), the Fisher-based mask in Eq. (6), the sensitivity score in Eq. (9), and the aggregation in Eq. (10) are all defined independently and then evaluated on SVHN, CIFAR-10, and CIFAR-100 against external baselines; no fitted parameter is renamed as a prediction. Hyperparameters are tuned on one configuration (CIFAR-10, s=4) and transferred to other datasets, which is standard generalization testing rather than circularity. The Learngene citations [18,19,33-42] are used as motivation and terminology, not as a load-bearing uniqueness theorem or ansatz, so they do not make the empirical results tautological. The strongest caveat is the communication accounting: Section IV-B defines Comm as uploads only (R*B*|W|*2), while Algorithms 1 and 2 require the full cluster model Theta_k to be downloaded each round; this makes the advertised 4x saving definitional under one-way accounting and is a validity/measurement concern, but it is not a circular derivation of a scientific prediction from the method's own inputs. The ambiguity in Eq. (9) about cosine similarity and personalization is a correctness concern, not circularity. Overall, the paper's central quantitative results are externally benchmarked and not forced by definition.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central claim rests on several heuristics (Fisher-based generalization, layer sensitivity via cosine, clustering accuracy) and a set of tuned hyperparameters (γ, ε, λ1, λ2, K, d). The learnGene is an invented construct with no independent validation. The axioms are domain assumptions about what constitutes useful transferable knowledge, none of which are proven.

free parameters (5)
  • γ (number of layers retained in learnGene) = not reported
    Section III-A and Algorithm 1 state the mask keeps the top γ layers, and that γ is determined by a performance-based adaptive procedure. No value or procedure is specified, making the learned gene size a free parameter that directly affects communication cost and accuracy.
  • ε (Fisher information threshold) = 0.5
    Section III-A uses ε to decide whether a parameter belongs to the elastic learnGene. The best value 0.5 is reported after hyperparameter tuning in Table VI on CIFAR-10 s=4.
  • λ1, λ2 (loss weights) = approximately 0.5 and 0.05
    The total loss Lall = Lcls + λ1 Lgen + λ2 Lelg is defined in Section III-A. The values are tuned on CIFAR-10 s=4 (Table VI) and then applied to all other datasets.
  • K (number of clusters) = 4
    Section IV-A sets K=4 and validates the choice in Table VI on CIFAR-10 s=4; the value is then used for all experiments.
  • d (SVD dimension for client data subspace) = 5
    Section III-C sets d=5 following the clustering method of Vahidian et al. [20]. This controls the size of the vector used for cluster assignment.
assumptions (4)
  • domain assumption Parameters with high Fisher information encapsulate generalizable knowledge that should be preserved as the elastic learnGene.
    Section III-A justifies the elastic learnGene construction in Eq. (5) and Eq. (6) based on Fisher values, but no proof or evidence is given that high-Fisher parameters are more transferable across data distributions.
  • domain assumption The cosine similarity in Eq. (9) between previous and updated local model parameters indicates which layers are personalized versus generalizable, and the selected layers can serve as a learnGene for initialization.
    Section III-A defines the sensitivity score ξ using cosine similarity. The paper's own interpretation contradicts the mathematical meaning of cosine, so the validity of this selection rule is uncertain and load-bearing for the learnGene contents.
  • domain assumption The one-shot clustering method of Vahidian et al. [20], which uses SVD-based principal angles between client data subspaces, correctly groups clients with similar data distributions.
    Section III-B and III-C rely on this clustering to define cluster models and to assign new agnostic clients to the nearest cluster using Eq. (12). No analysis of clustering error is provided.
  • domain assumption Under DAFL, label distributions differ across clients, class-conditional distributions p(x|y) are invariant, and new clients have disjoint label sets from known clients.
    Definition 1 in Section III formalizes this scenario, and the experiments simulate it. The method's generalization claims depend on this separation being realistic.
invented entities (1)
  • learnGene
    purpose: A small subset of model parameters selected by the framework (via Fisher thresholding and layer-wise sensitivity masking) that is communicated between clients and server, and used to initialize agnostic client models.
    The learnGene is entirely defined by the proposed method. It has no falsifiable handle outside the paper, such as a predicted measurable quantity; its utility is only demonstrated end-to-end on the paper's benchmark experiments.

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Cite this review

Pith. "Pith review of GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning." pith.science (2026). https://pith.science/paper/MUOMERZ6

@misc{pith2026250414628,
  author       = {Pith},
  title        = {Pith review of: GENE-FL: Gene-Driven Parameter-Efficient Dynamic Federated Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MUOMERZ6}},
  note         = {Machine review of arXiv:2504.14628}
}
abstract

Real-world \underline{F}ederated \underline{L}earning systems often encounter \underline{D}ynamic clients with \underline{A}gnostic and highly heterogeneous data distributions (DAFL), which pose challenges for efficient communication and model initialization. To address these challenges, we draw inspiration from the recently proposed Learngene paradigm, which compresses the large-scale model into lightweight, cross-task meta-information fragments. Learngene effectively encapsulates and communicates core knowledge, making it particularly well-suited for DAFL, where dynamic client participation requires communication efficiency and rapid adaptation to new data distributions. Based on this insight, we propose a Gene-driven parameter-efficient dynamic Federated Learning (GENE-FL) framework. First, local models perform quadratic constraints based on parameters with high Fisher values in the global model, as these parameters are considered to encapsulate generalizable knowledge. Second, we apply the strategy of parameter sensitivity analysis in local model parameters to condense the \textit{learnGene} for interaction. Finally, the server aggregates these small-scale trained \textit{learnGene}s into a robust \textit{learnGene} with cross-task generalization capability, facilitating the rapid initialization of dynamic agnostic client models. Extensive experimental results demonstrate that GENE-FL reduces \textbf{4 $\times$} communication costs compared to FEDAVG and effectively initializes agnostic client models with only about \textbf{9.04} MB.

Figures

Figures reproduced from arXiv: 2504.14628 by the authors.

Figure 1
Figure 1. Illustration of Dynamic Agnostic Federated Learning and Learngene. In the accumulating, condensing, and inheriting processes of the Learngene, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of training process of GENE-FL, which includes (I) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Performance curve comparison on CIFAR-10 with [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Higher privacy protection. Reconstructing images under iDLG attacks [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Comparison of communication cost curves from client to server over [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]

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Reference graph

Works this paper leans on

59 extracted references · 33 canonical work pages

  1. [1]

    Communication-efficient learning of deep networks from decentralized data,

    B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial Intelligence and Statistics , 2017, pp. 1273–1282

    2017

  2. [2]

    Calfat: Calibrated federated ad- versarial training with label skewness,

    C. Chen, Y . Liu, X. Ma, and L. Lyu, “Calfat: Calibrated federated ad- versarial training with label skewness,” Advances in Neural Information Processing Systems, vol. 35, pp. 3569–3581, 2022

    work page 2022

  3. [3]

    Cross-silo feature space alignment for federated learning on clients with imbalanced data,

    Z. Qi, L. Meng, Z. Li, H. Hu, and X. Meng, “Cross-silo feature space alignment for federated learning on clients with imbalanced data,” 2025

    2025

  4. [4]

    Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets,

    D. Ng, X. Lan, M. M.-S. Yao, W. P. Chan, and M. Feng, “Federated learning: a collaborative effort to achieve better medical imaging models for individual sites that have small labelled datasets,” Quantitative Imaging in Medicine and Surgery , vol. 11, no. 2, p. 852, 2021

    work page 2021

  5. [5]

    Federated learning for medical image analysis: A survey,

    H. Guan, P.-T. Yap, A. Bozoki, and M. Liu, “Federated learning for medical image analysis: A survey,” Pattern Recognition , p. 110424, 2024

    work page 2024

  6. [6]

    Personalized federated learning under mixture of distribu- tions,

    Y . Wu, S. Zhang, W. Yu, Y . Liu, Q. Gu, D. Zhou, H. Chen, and W. Cheng, “Personalized federated learning under mixture of distribu- tions,” in International Conference on Machine Learning. PMLR, 2023, pp. 37 860–37 879

    work page 2023

  7. [7]

    Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences,

    M. Imran, H. Yin, T. Chen, Q. V . H. Nguyen, A. Zhou, and K. Zheng, “Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences,” ACM Transactions on Information Systems, vol. 41, no. 3, pp. 1–30, 2023

    2023

  8. [8]

    Joint air-ground distributed federated learning for intelligent transportation systems,

    S. S. Shinde and D. Tarchi, “Joint air-ground distributed federated learning for intelligent transportation systems,” IEEE Transactions on Intelligent Transportation Systems, 2023

    work page 2023

Show all 59 references
  1. [9]

    Federated learning for smart cities: A comprehensive survey,

    S. Pandya, G. Srivastava, R. Jhaveri, M. R. Babu, S. Bhattacharya, P. K. R. Maddikunta, S. Mastorakis, M. J. Piran, and T. R. Gadekallu, “Federated learning for smart cities: A comprehensive survey,” Sustain- able Energy Technologies and Assessments , vol. 55, p. 102987, 2023

    2023

  2. [10]

    Scaffold: Stochastic controlled averaging for federated learn- ing,

    S. P. Karimireddy, S. Kale, M. Mohri, S. Reddi, S. Stich, and A. T. Suresh, “Scaffold: Stochastic controlled averaging for federated learn- ing,” in International conference on machine learning . PMLR, 2020, pp. 5132–5143

    2020

  3. [11]

    Feder- ated learning with compression: Unified analysis and sharp guarantees,

    F. Haddadpour, M. M. Kamani, A. Mokhtari, and M. Mahdavi, “Feder- ated learning with compression: Unified analysis and sharp guarantees,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 2350–2358

    2021

  4. [12]

    Fedfisher: Leveraging fisher information for one-shot federated learning,

    D. Jhunjhunwala, S. Wang, and G. Joshi, “Fedfisher: Leveraging fisher information for one-shot federated learning,” inInternational Conference on Artificial Intelligence and Statistics . PMLR, 2024, pp. 1612–1620

    2024

  5. [13]

    One-shot federated learning for leo con- stellations that reduces convergence time from days to 90 minutes,

    M. Elmahallawy and T. Luo, “One-shot federated learning for leo con- stellations that reduces convergence time from days to 90 minutes,” in 2023 24th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2023, pp. 45–54

    2023

  6. [14]

    Dense: Data-free one-shot federated learning,

    J. Zhang, C. Chen, B. Li, L. Lyu, S. Wu, S. Ding, C. Shen, and C. Wu, “Dense: Data-free one-shot federated learning,” Advances in Neural Information Processing Systems , vol. 35, pp. 21 414–21 428, 2022

    2022

  7. [15]

    One-shot empirical privacy estimation for feder- ated learning,

    G. Andrew, P. Kairouz, S. Oh, A. Oprea, H. B. McMahan, and V . M. Suriyakumar, “One-shot empirical privacy estimation for feder- ated learning,” in The Twelfth International Conference on Learning Representations, 2024, pp. 1–21

    2024

  8. [16]

    Fedproto: Federated prototype learning across heterogeneous clients,

    Y . Tan, G. Long, L. Liu, T. Zhou, Q. Lu, J. Jiang, and C. Zhang, “Fedproto: Federated prototype learning across heterogeneous clients,” in AAAI Conference on Artificial Intelligence , vol. 1, 2022, pp. 3–19

    2022

  9. [17]

    Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning,

    J. Zhang, Y . Liu, Y . Hua, and J. Cao, “Fedtgp: Trainable global prototypes with adaptive-margin-enhanced contrastive learning for data and model heterogeneity in federated learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 15, 2024, pp...

    2024

  10. [18]

    Learn- gene: From open-world to your learning task,

    Q.-F. Wang, X. Geng, S.-X. Lin, S.-Y . Xia, L. Qi, and N. Xu, “Learn- gene: From open-world to your learning task,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 8, 2022, pp. 8557–8565

    2022

  11. [19]

    Learngene: Inheriting condensed knowledge from the ancestry model to descendant models,

    Q. Wang, X. Yang, S. Lin, and X. Geng, “Learngene: Inheriting condensed knowledge from the ancestry model to descendant models,” arXiv preprint arXiv:2305.02279 , pp. 1–17, 2023

    2023 arXiv

  12. [20]

    Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,

    S. Vahidian, M. Morafah, W. Wang, V . Kungurtsev, C. Chen, M. Shah, and B. Lin, “Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces,” in Proceedings of the AAAI Conference on Artificial Intelligen...

    2023

  13. [21]

    Cross-silo prototypical calibration for federated learning with non-iid data,

    Z. Qi, L. Meng, Z. Chen, H. Hu, H. Lin, and X. Meng, “Cross-silo prototypical calibration for federated learning with non-iid data,” in Proceedings of the 31st ACM International Conference on Multimedia , 2023, pp. 3099–3107

    2023

  14. [22]

    Dafkd: Domain-aware federated knowledge distillation,

    H. Wang, Y . Li, W. Xu, R. Li, Y . Zhan, and Z. Zeng, “Dafkd: Domain-aware federated knowledge distillation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 20 412–20 421

    2023

  15. [23]

    Fedproc: Prototypical contrastive federated learning on non-iid data,

    X. Mu, Y . Shen, K. Cheng, X. Geng, J. Fu, T. Zhang, and Z. Zhang, “Fedproc: Prototypical contrastive federated learning on non-iid data,” arXiv preprint arXiv:2109.12273 , 2021

    2021 arXiv

  16. [24]

    Federated learning with hierarchical clustering of local updates to improve training on non-iid data,

    C. Briggs, Z. Fan, and P. Andras, “Federated learning with hierarchical clustering of local updates to improve training on non-iid data,” in International Joint Conference on Neural Networks . IEEE, 2020, pp. 1–9

    2020

  17. [25]

    Preservation of the global knowledge by not-true distillation in federated learning,

    G. Lee, M. Jeong, Y . Shin, S. Bae, and S.-Y . Yun, “Preservation of the global knowledge by not-true distillation in federated learning,” Ad- vances in Neural Information Processing Systems , vol. 35, pp. 38 461– 38 474, 2022

    2022

  18. [26]

    Fedcompass: Efficient cross-silo federated learning on heterogeneous client devices using a computing power-aware scheduler,

    Z. Li, P. Chaturvedi, S. He, H. Chen, G. Singh, V . Kindratenko, E. A. Huerta, K. Kim, and R. Madduri, “Fedcompass: Efficient cross-silo federated learning on heterogeneous client devices using a computing power-aware scheduler,” in The Twelfth International Conference on Lear...

    2024

  19. [27]

    Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,

    L. Yi, X. Shi, N. Wang, J. Zhang, G. Wang, and X. Liu, “Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,” IEEE Transactions on Mobile Computing , 2024

    2024

  20. [28]

    Com- putation and communication efficient federated learning with adaptive model pruning,

    Z. Jiang, Y . Xu, H. Xu, Z. Wang, J. Liu, Q. Chen, and C. Qiao, “Com- putation and communication efficient federated learning with adaptive model pruning,” IEEE Transactions on Mobile Computing , 2023

    2023

  21. [29]

    Complement sparsification: Low-overhead model pruning for federated learning,

    X. Jiang and C. Borcea, “Complement sparsification: Low-overhead model pruning for federated learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 7, 2023, pp. 8087– 8095

    2023

  22. [30]

    Efficient federated learning with enhanced privacy via lottery ticket pruning in edge computing,

    Y . Shi, K. Wei, L. Shen, J. Li, X. Wang, B. Yuan, and S. Guo, “Efficient federated learning with enhanced privacy via lottery ticket pruning in edge computing,” IEEE Transactions on Mobile Computing , 2024

    2024

  23. [31]

    Expanding the reach of federated learning by reducing client resource require- ments,

    S. Caldas, J. Kone ˇcny, H. B. McMahan, and A. Talwalkar, “Expanding the reach of federated learning by reducing client resource require- ments,” arXiv preprint arXiv:1812.07210 , pp. 1–12, 2018

    2018 arXiv

  24. [32]

    Fedlps: Heterogeneous federated learning for multiple tasks with local parameter sharing,

    Y . Jia, X. Zhang, A. Beheshti, and W. Dou, “Fedlps: Heterogeneous federated learning for multiple tasks with local parameter sharing,” 2024

    2024

  25. [33]

    Linearly decomposing and recomposing vision transformers for diverse-scale models,

    S. Lin, M. Zhang, R. Chen, X. Yang, Q. Wang, and X. Geng, “Linearly decomposing and recomposing vision transformers for diverse-scale models,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024, pp. 1–25

    2024

  26. [34]

    Initializing variable-sized vision transformers from learngene with learnable transformation,

    S. Xia, Y . Zu, X. Yang, and X. Geng, “Initializing variable-sized vision transformers from learngene with learnable transformation,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024, pp. 1–26

    2024

  27. [35]

    Trans- former as linear expansion of learngene,

    S. Xia, M. Zhang, X. Yang, R. Chen, H. Chen, and X. Geng, “Trans- former as linear expansion of learngene,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 14, 2024, pp. 16 014– 16 022

    2024

  28. [36]

    Vision transformers as probabilistic expansion from learngene,

    Q. Wang, X. Yang, H. Chen, and X. Geng, “Vision transformers as probabilistic expansion from learngene,” in Forty-first International Conference on Machine Learning , 2024, pp. 1–14

    2024

  29. [37]

    Cluster-learngene: In- heriting adaptive clusters for vision transformers,

    Q. Wang, X. Yang, F. Feng, X. Geng et al. , “Cluster-learngene: In- heriting adaptive clusters for vision transformers,” in The Thirty-eighth Annual Conference on Neural Information Processing Systems , 2024, pp. 1–23

    2024

  30. [38]

    Facilitating ai-based csi feedback deployment in massive mimo systems with learngene,

    X. Li, J. Guo, C.-K. Wen, X. Geng, and S. Jin, “Facilitating ai-based csi feedback deployment in massive mimo systems with learngene,” IEEE Transactions on Wireless Communications , 2024

    2024

  31. [39]

    Wave: Weight template for adaptive initialization of variable-sized models,

    F. Feng, Y . Xie, J. Wang, and X. Geng, “Wave: Weight template for adaptive initialization of variable-sized models,” arXiv preprint arXiv:2406.17503, 2024

    2024 arXiv

  32. [40]

    Kind: Knowl- edge integration and diversion in diffusion models,

    Y . Xie, F. Feng, J. Wang, X. Geng, and Y . Rui, “Kind: Knowl- edge integration and diversion in diffusion models,” arXiv preprint arXiv:2408.07337, 2024

    2024 arXiv

  33. [41]

    Building variable-sized models via learngene pool,

    B. Shi, S. Xia, X. Yang, H. Chen, Z. Kou, and X. Geng, “Building variable-sized models via learngene pool,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 13, 2024, pp. 14 946– 14 954

    2024

  34. [42]

    Transferring core knowledge via learngenes,

    F. Feng, J. Wang, and X. Geng, “Transferring core knowledge via learngenes,” arXiv preprint arXiv:2401.08139 , 2024. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021 12

    2024 arXiv

  35. [43]

    Objective assessment of image quality. ii. fisher information, fourier crosstalk, and figures of merit for task performance,

    H. H. Barrett, J. Denny, R. F. Wagner, and K. J. Myers, “Objective assessment of image quality. ii. fisher information, fourier crosstalk, and figures of merit for task performance,” JOSA A, vol. 12, no. 5, pp. 834–852, 1995

    1995

  36. [44]

    A tutorial on fisher information,

    A. Ly, M. Marsman, J. Verhagen, R. P. Grasman, and E.-J. Wagenmakers, “A tutorial on fisher information,” Journal of Mathematical Psychology, vol. 80, pp. 40–55, 2017

    2017

  37. [45]

    The adversarial attack and detection under the fisher information metric,

    C. Zhao, P. T. Fletcher, M. Yu, Y . Peng, G. Zhang, and C. Shen, “The adversarial attack and detection under the fisher information metric,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 5869–5876

    2019

  38. [46]

    Neural fim for learning fisher information metrics from point cloud data,

    O. Fasina, G. Huguet, A. Tong, Y . Zhang, G. Wolf, M. Nickel, I. Adel- stein, and S. Krishnaswamy, “Neural fim for learning fisher information metrics from point cloud data,” in International Conference on Machine Learning. PMLR, 2023, pp. 9814–9826

    2023

  39. [47]

    Towards a theoretical and practical understanding of one-shot federated learning with fisher infor- mation,

    D. Jhunjhunwala, S. Wang, and G. Joshi, “Towards a theoretical and practical understanding of one-shot federated learning with fisher infor- mation,” in Federated Learning and Analytics in Practice: Algorithms, Systems, Applications, and Opportunities , 2023, pp. 1–19

    2023

  40. [48]

    Use and abuse of the fisher information matrix in the assessment of gravitational-wave parameter-estimation prospects,

    M. Vallisneri, “Use and abuse of the fisher information matrix in the assessment of gravitational-wave parameter-estimation prospects,” Physical Review D , vol. 77, no. 4, p. 042001, 2008

    2008

  41. [49]

    Layer-wise relevance propagation for neural networks with local renor- malization layers,

    A. Binder, G. Montavon, S. Lapuschkin, K.-R. M ¨uller, and W. Samek, “Layer-wise relevance propagation for neural networks with local renor- malization layers,” in Artificial Neural Networks and Machine Learning– ICANN 2016: 25th International Conference on Artificial Neural N...

    2016

  42. [50]

    Elastic weight removal for faithful and abstractive dialogue generation,

    N. Daheim, N. Dziri, M. Sachan, I. Gurevych, and E. M. Ponti, “Elastic weight removal for faithful and abstractive dialogue generation,” arXiv e-prints, pp. arXiv–2303, 2023

    2023

  43. [51]

    Beyond symmetry: Best submatrix selection for the sparse truncated svd,

    Y . Li and W. Xie, “Beyond symmetry: Best submatrix selection for the sparse truncated svd,” Mathematical Programming, vol. 208, no. 1, pp. 1–50, 2024

    2024

  44. [52]

    Deep leakage from gradients,

    L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,” Advances in neural information processing systems , vol. 32, 2019

    2019

  45. [53]

    Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning,

    Y . Wu, Y . Kang, J. Luo, Y . He, and Q. Yang, “Fedcg: Leverage conditional gan for protecting privacy and maintaining competitive performance in federated learning,” arXiv preprint arXiv:2111.08211 , pp. 1–8, 2021

    2021 arXiv

  46. [54]

    Partialfed: Cross-domain personalized federated learning via partial initialization,

    B. Sun, H. Huo, Y . Yang, and B. Bai, “Partialfed: Cross-domain personalized federated learning via partial initialization,” Advances in Neural Information Processing Systems , vol. 34, pp. 23 309–23 320, 2021

    2021

  47. [55]

    Reading digits in natural images with unsupervised feature learning,

    Y . Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, A. Y . Ng et al. , “Reading digits in natural images with unsupervised feature learning,” in NIPS workshop on deep learning and unsupervised feature learning , vol. 2011, no. 5. Granada, Spain, 2011, pp. 1–9

    2011

  48. [56]

    Learning multiple layers of features from tiny images,

    A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” pp. 1–60, 2009

    2009

  49. [57]

    Fedbn: Feder- ated learning on non-iid features via local batch normalization,

    X. Li, M. Jiang, X. Zhang, M. Kamp, and Q. Dou, “Fedbn: Feder- ated learning on non-iid features via local batch normalization,” arXiv preprint arXiv:2102.07623, 2021

    2021 arXiv

  50. [58]

    Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning,

    Z. Zhu, Y . Shi, J. Luo, F. Wang, C. Peng, P. Fan, and K. B. Letaief, “Fedlp: Layer-wise pruning mechanism for communication-computation efficient federated learning,” in ICC 2023-IEEE International Confer- ence on Communications . IEEE, 2023, pp. 1250–1255

    2023

  51. [59]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

    2016

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.