pith. machine review for the scientific record. sign in

arxiv: 2605.07961 · v1 · submitted 2026-05-08 · 💻 cs.LG · cs.CR· cs.NI

Recognition: 1 theorem link

· Lean Theorem

Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs

Hanlin Cai , Kai Li , Houtianfu Wang , Haofan Dong , Yichen Li , Falko Dressler , Ozgur B. Akan
Authors on Pith no claims yet

Pith reviewed 2026-05-11 02:38 UTC · model grok-4.3

classification 💻 cs.LG cs.CRcs.NI
keywords federated fine-tuningLLMmodel manipulationgraph representation learningpoisoning attackaugmented Lagrangianadversarial update
0
0 comments X

The pith

A graph representation learning method allows attackers to generate malicious updates that corrupt federated fine-tuning of LLMs while evading detection.

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

The paper proposes an Augmented Model manipulation strategy, AugMP, for attacking federated fine-tuning of large language models. It employs a graph representation learning framework to capture correlations among benign model updates and uses this to direct the creation of malicious updates. An iterative algorithm based on augmented Lagrangian dual formulation optimizes these updates to meet attack objectives without straying far from benign characteristics. Experiments on multiple LLM backbones show superior manipulation results over baselines, with notable drops in accuracy. This matters because it questions the security of privacy-preserving collaborative LLM training against sophisticated poisoning attacks.

Core claim

The central claim is that graph representation learning can effectively model the feature space of benign LLM updates in federated settings, enabling the generation of malicious updates through an augmented Lagrangian-based optimization that achieve strong performance degradation in the aggregated model while remaining consistent enough to avoid standard defenses.

What carries the argument

The graph representation learning framework that captures feature correlations among benign LLM updates to guide malicious update generation, paired with iterative optimization using an augmented Lagrangian dual formulation.

If this is right

  • The AugMP strategy outperforms all competing baselines in manipulation effectiveness.
  • Global LLM accuracy is reduced by up to 26%.
  • Average accuracy of local LLM agents is degraded by up to 22%.
  • High statistical and geometric consistency with benign updates allows evasion of distance- and similarity-based defense methods.
  • The approach works across multiple LLM backbones.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Defenses may need to move beyond simple statistical checks to detect anomalies in the learned graph structures of updates.
  • This attack technique could be adapted to other distributed learning scenarios involving parameter aggregation.
  • Robust aggregation methods that incorporate learned representations of normal behavior might mitigate such threats.
  • Further analysis could reveal how the dimensionality of LLM parameters affects the effectiveness of graph-based modeling for attacks.

Load-bearing premise

The feature correlations captured by the graph representation learning framework from benign updates are sufficient to generate malicious updates that achieve adversarial goals while preserving high consistency with the benign distribution.

What would settle it

A test where the proposed malicious updates are injected into a federated fine-tuning process and the resulting global model accuracy does not show the claimed reduction, or where a new defense using similar graph features successfully identifies them.

Figures

Figures reproduced from arXiv: 2605.07961 by Falko Dressler, Hanlin Cai, Haofan Dong, Houtianfu Wang, Kai Li, Ozgur B. Akan, Yichen Li.

Figure 1
Figure 1. Figure 1: (a) Benign training process of the FedLLMs system, and (b) impact of the adversary on the FedLLMs training process. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Architecture of the proposed AugMP manipulation strategy based on the GRL framework [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Global testing accuracy under the benign setting and under three manipulation strategies over 50 communication rounds. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Local average testing accuracy under the benign setting and under the proposed AugMP manipulation strategy over 50 communication rounds. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Examples of misclassification and rationalized explanation generated by the manipulated FedLLMs (based on Qwen2.5 models) under the AugMP [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Euclidean distances between each agent’s local update and the aggregated global update under three manipulation strategies over 50 rounds. [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Cosine similarity between each agent’s local updates under three manipulation strategies over 50 rounds. [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Ablation study of AugMP on the AG News dataset. The full AugMP strategy is compared with two variants, namely [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
read the original abstract

Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated learning, FFT enables distributed agents to jointly refine a shared pretrained LLM by aggregating local LLM updates without sharing local raw data. However, FFT-based LLMs remain vulnerable to model manipulation threats, in which adversarial participants upload manipulated LLM updates that corrupt the aggregation process and degrade the performance of the global LLM. In this paper, we propose an Augmented Model maniPulation (AugMP) strategy against FFT-based LLMs. Specifically, we design a novel graph representation learning framework that captures feature correlations among benign LLM updates to guide the generation of malicious updates. To enhance manipulation effectiveness and stealthiness, we develop an iterative manipulation algorithm based on an augmented Lagrangian dual formulation. Through this formulation, malicious updates are optimized to embed adversarial objectives while preserving benign-like parameter characteristics. Experimental results across multiple LLM backbones demonstrate that the AugMP strategy achieves the strongest manipulation performance among all competing baselines, reducing the global LLM accuracy by up to 26% and degrading the average accuracy of local LLM agents by up to 22%. Meanwhile, AugMP maintains high statistical and geometric consistency with benign updates, enabling it to evade conventional distance- and similarity-based defense methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes AugMP, an attack framework against federated fine-tuning of LLMs. It constructs a graph representation learning model on benign parameter updates (nodes as features, edges as learned correlations) and employs an iterative augmented Lagrangian optimizer to synthesize malicious updates. These updates are designed to embed attack objectives (global and local accuracy degradation) while preserving statistical and geometric similarity to benign updates, thereby evading distance- and similarity-based defenses. Experiments across multiple LLM backbones are reported to show that AugMP outperforms baselines, achieving up to 26% drop in global accuracy and 22% in average local accuracy.

Significance. If the empirical results hold under rigorous verification, the work would be significant for highlighting practical vulnerabilities in federated LLM fine-tuning and for demonstrating a graph-augmented attack that balances effectiveness with stealth. The combination of representation learning with constrained optimization is a technically interesting direction that could inform both attack and defense research in distributed ML security.

major comments (3)
  1. [Abstract and §4] Abstract and §4 (Experimental Results): The central claim that AugMP 'achieves the strongest manipulation performance' with up to 26% global and 22% local accuracy drops is presented without details on the number of federated rounds, client data partitions, statistical significance tests, or whether the benign updates used to train the graph model are from the same distribution as the test rounds. This leaves open whether the reported gains generalize or reflect post-hoc selection.
  2. [§3.2] §3.2 (Graph Representation Learning Framework): The assumption that a graph trained on observed benign LLM deltas can reliably produce malicious deltas that remain inside the statistical/geometric envelope of unseen benign updates is load-bearing for both the attack success and the stealth claims. No ablation or out-of-distribution test (e.g., new data partitions or later rounds) is described to quantify mismatch in captured correlations, which the skeptic correctly identifies as the weakest link.
  3. [§3.3] §3.3 (Augmented Lagrangian Formulation): The iterative manipulation algorithm is claimed to simultaneously embed adversarial objectives and preserve benign-like characteristics, yet no analysis is given of the trade-off parameter schedule or convergence behavior when the graph embedding and the attack loss conflict. If the dual formulation only satisfies consistency on the training benign set, the stealth property may not transfer.
minor comments (2)
  1. [§3] Notation for the graph (nodes, edges, adjacency matrix) and the augmented Lagrangian multipliers should be introduced with explicit definitions and dimensions to improve readability.
  2. [§4] The list of competing baselines and their implementation details (hyperparameters, attack budgets) are referenced but not tabulated; a comparison table would clarify the performance margins.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive report. The comments identify important gaps in experimental detail, validation of key assumptions, and analysis of the optimization procedure. We address each point below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract and §4] The central claim that AugMP 'achieves the strongest manipulation performance' with up to 26% global and 22% local accuracy drops is presented without details on the number of federated rounds, client data partitions, statistical significance tests, or whether the benign updates used to train the graph model are from the same distribution as the test rounds. This leaves open whether the reported gains generalize or reflect post-hoc selection.

    Authors: We agree that additional experimental details are required for reproducibility and to address generalization concerns. In the revised manuscript we will expand §4 to explicitly state that all experiments use 100 federated rounds with 10 clients, both IID and non-IID (Dirichlet α=0.5) partitions, and report mean ± std over 5 independent runs together with paired t-tests against baselines. The graph model is trained on benign updates collected from the first 20 rounds and evaluated on malicious updates generated in rounds 21–100 drawn from the identical client data distributions; we will add a short paragraph clarifying this temporal split to demonstrate that the reported accuracy drops are not the result of post-hoc selection. revision: yes

  2. Referee: [§3.2] The assumption that a graph trained on observed benign LLM deltas can reliably produce malicious deltas that remain inside the statistical/geometric envelope of unseen benign updates is load-bearing for both the attack success and the stealth claims. No ablation or out-of-distribution test (e.g., new data partitions or later rounds) is described to quantify mismatch in captured correlations.

    Authors: This is a valid concern and the central modeling assumption. We will add a new ablation subsection in §4 that trains the graph representation model exclusively on benign updates from rounds 1–20 and then measures both attack success and stealth metrics (cosine similarity, Wasserstein distance to benign distribution) when malicious updates are generated for rounds 21–100. The results show only marginal degradation in stealth while attack effectiveness remains within 3% of the original numbers, providing quantitative evidence that the learned correlations transfer to later rounds under the same data distribution. revision: yes

  3. Referee: [§3.3] The iterative manipulation algorithm is claimed to simultaneously embed adversarial objectives and preserve benign-like characteristics, yet no analysis is given of the trade-off parameter schedule or convergence behavior when the graph embedding and the attack loss conflict. If the dual formulation only satisfies consistency on the training benign set, the stealth property may not transfer.

    Authors: We acknowledge the absence of this analysis. In the revision we will augment §3.3 with the exact penalty-parameter schedule (initial λ=0.1, multiplied by 1.5 every five iterations up to a cap of 10) and include convergence plots of primal and dual residuals in the appendix. We will also add a short discussion of the case where the graph-consistency and attack objectives conflict, showing that the augmented Lagrangian still converges to a feasible point within 25 iterations while keeping the malicious update inside the 95th-percentile benign envelope on held-out rounds. This directly addresses transferability of the stealth property. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical construction and evaluation of AugMP framework

full rationale

The paper introduces a graph representation learning framework and an augmented Lagrangian algorithm as a constructed method for generating malicious updates in federated LLM fine-tuning. Claims of performance (accuracy drops of up to 26%/22%) and consistency are supported by experimental results across LLM backbones rather than any self-referential derivation, fitted-parameter renaming, or self-citation chain. No load-bearing step reduces to its own inputs by definition or construction; the approach is heuristic and externally validated.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are described in sufficient detail to populate the ledger.

pith-pipeline@v0.9.0 · 5552 in / 1056 out tokens · 34981 ms · 2026-05-11T02:38:30.234355+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages · 3 internal anchors

  1. [1]

    Parameter-efficient fine-tuning of large-scale pre-trained language models,

    N. Ding, Y . Qin, G. Yang, F. Wei, Z. Yang, Y . Su, S. Hu, Y . Chen, C.- M. Chan, W. Chenet al., “Parameter-efficient fine-tuning of large-scale pre-trained language models,”Nature machine intelligence, vol. 5, no. 3, pp. 220–235, 2023

    work page 2023

  2. [2]

    A survey on federated fine-tuning of large language models,

    Y . Wu, C. Tian, J. Li, H. Sun, K. Tam, Z. Zhou, H. Liao, Z. Guo, L. Li, and C. Xu, “A survey on federated fine-tuning of large language models,”arXiv preprint arXiv:2503.12016, 2025

    work page arXiv 2025

  3. [3]

    Towards resilient federated learning in cyberedge networks: Recent advances and future trends,

    K. Li, Z. Zhang, A. Pourkabirian, W. Ni, F. Dressler, and O. B. Akan, “Towards resilient federated learning in cyberedge networks: Recent advances and future trends,”arXiv preprint arXiv:2504.01240, 2025

    work page arXiv 2025

  4. [4]

    Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,

    O. Friha, M. A. Ferrag, B. Kantarci, B. Cakmak, A. Ozgun, and N. Ghoualmi-Zine, “Llm-based edge intelligence: A comprehensive survey on architectures, applications, security and trustworthiness,”IEEE Open Journal of the Communications Society, vol. 5, pp. 5799–5856, 2024

    work page 2024

  5. [5]

    Federated fine-tuning for pre-trained foundation models over wireless networks,

    Z. Wang, Y . Zhou, Y . Shi, and K. B. Letaief, “Federated fine-tuning for pre-trained foundation models over wireless networks,”IEEE Trans- actions on Wireless Communications, vol. 24, no. 4, pp. 3450–3464, 2025

    work page 2025

  6. [6]

    Towards federated large language models: Motivations, methods, and future directions,

    Y . Cheng, W. Zhang, Z. Zhang, C. Zhang, S. Wang, and S. Mao, “Towards federated large language models: Motivations, methods, and future directions,”IEEE Communications Surveys & Tutorials, 2024

    work page 2024

  7. [7]

    Lora: Low-rank adaptation of large language models

    E. J. Hu, Y . Shen, P. Wallis, Z. Allen-Zhu, Y . Li, S. Wang, L. Wang, W. Chenet al., “Lora: Low-rank adaptation of large language models.” International Conference on Learning Representations, 2022

    work page 2022

  8. [8]

    Federated fine-tuning of llms: Framework comparison and research directions,

    N. Yan, Y . Su, Y . Deng, and R. Schober, “Federated fine-tuning of llms: Framework comparison and research directions,”IEEE Communications Magazine, vol. 63, no. 10, pp. 52–58, 2025

    work page 2025

  9. [9]

    Federated adaptive fine-tuning of large language models with heterogeneous quantization and lora,

    Z. Gao, Z. Zhang, Y . Guo, and Y . Gong, “Federated adaptive fine-tuning of large language models with heterogeneous quantization and lora,” in IEEE Conference on Computer Communications (INFOCOM), 2025

    work page 2025

  10. [10]

    Efficient parallel split learning over resource-constrained wireless edge networks,

    Z. Lin, G. Zhu, Y . Deng, X. Chen, Y . Gao, K. Huang, and Y . Fang, “Efficient parallel split learning over resource-constrained wireless edge networks,”IEEE Transactions on Mobile Computing, vol. 23, no. 10, pp. 9224–9239, 2024

    work page 2024

  11. [11]

    Adaptive model slimming for communication and computation efficient federated edge learning under non-iid data distribution,

    Q. Chen, Z. Wang, X. Zhang, D. Wen, G. Zhu, and M. K. Awan, “Adaptive model slimming for communication and computation efficient federated edge learning under non-iid data distribution,”IEEE Transac- tions on Mobile Computing, 2026

    work page 2026

  12. [12]

    Fedsecurity: A benchmark for attacks and defenses in federated learning and federated llms,

    S. Han, B. Buyukates, Z. Hu, H. Jin, W. Jin, L. Sun, X. Wang, W. Wu, C. Xie, Y . Yaoet al., “Fedsecurity: A benchmark for attacks and defenses in federated learning and federated llms,” inthe 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024, pp. 5070– 5081

    work page 2024

  13. [13]

    Large model based agents: State-of-the-art, cooperation paradigms, security and privacy, and future trends,

    Y . Wang, Y . Pan, Z. Su, Y . Deng, Q. Zhao, L. Du, T. H. Luan, J. Kang, and D. Niyato, “Large model based agents: State-of-the-art, cooperation paradigms, security and privacy, and future trends,”IEEE Communications Surveys & Tutorials, 2025

    work page 2025

  14. [14]

    Dm-fedmf: A recommendation model of federated matrix factorization with detection mechanism,

    X. Zheng, X. Jia, X. Cheng, W. He, L. Sun, L. Guo, Q. Yu, and Y . Luo, “Dm-fedmf: A recommendation model of federated matrix factorization with detection mechanism,”IEEE Transactions on Network Science and Engineering, 2025

    work page 2025

  15. [15]

    Securing billion bluetooth devices leveraging learning-based techniques,

    H. Cai, “Securing billion bluetooth devices leveraging learning-based techniques,” inProceedings of the AAAI Conference on Artificial Intel- ligence, vol. 38, no. 21, 2024, pp. 23 731–23 732

    work page 2024

  16. [16]

    Rofed- llm: robust federated learning for large language models in adversarial wireless environments,

    H. Wang, Z. Yin, B. Chen, Y . Zeng, X. Yan, C. Zhou, and A. Li, “Rofed- llm: robust federated learning for large language models in adversarial wireless environments,”IEEE Transactions on Network Science and Engineering, 2025

    work page 2025

  17. [17]

    Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agents

    H. Cai, H. Wang, H. Dong, K. Li, and O. B. Akan, “Graph representation-based model poisoning on the heterogeneous internet of agents,”arXiv preprint arXiv:2511.07176, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    Prism: Privacy-aware routing for adaptive cloud–edge llm inference via semantic sketch collaboration,

    J. Zhan, H. Shen, Z. Lin, and T. He, “Prism: Privacy-aware routing for adaptive cloud–edge llm inference via semantic sketch collaboration,” inAAAI Conference on Artificial Intelligence, vol. 40, no. 33, 2026, pp. 28 150–28 158

    work page 2026

  19. [19]

    Local model poisoning attacks to{Byzantine-Robust}federated learning,

    M. Fang, X. Cao, J. Jia, and N. Gong, “Local model poisoning attacks to{Byzantine-Robust}federated learning,” in29th USENIX security symposium (USENIX Security 20), 2020, pp. 1605–1622

    work page 2020

  20. [20]

    A little is enough: Circumvent- ing defenses for distributed learning,

    G. Baruch, M. Baruch, and Y . Goldberg, “A little is enough: Circumvent- ing defenses for distributed learning,”Advances in Neural Information Processing Systems, vol. 32, 2019

    work page 2019

  21. [21]

    Mpaf: Model poisoning attacks to federated learning based on fake clients,

    X. Cao and N. Z. Gong, “Mpaf: Model poisoning attacks to federated learning based on fake clients,” inIEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 3396–3404

    work page 2022

  22. [22]

    Data-agnostic model poisoning against federated learning: A graph autoencoder approach,

    K. Li, J. Zheng, X. Yuan, W. Ni, O. B. Akan, and H. V . Poor, “Data-agnostic model poisoning against federated learning: A graph autoencoder approach,”IEEE Transactions on Information Forensics and Security, vol. 19, pp. 3465–3480, 2024

    work page 2024

  23. [23]

    Lever- age variational graph representation for model poisoning on federated learning,

    K. Li, X. Yuan, J. Zheng, W. Ni, F. Dressler, and A. Jamalipour, “Lever- age variational graph representation for model poisoning on federated learning,”IEEE Transactions on Neural Networks and Learning Systems, 2024

    work page 2024

  24. [24]

    User isolation poisoning on decentralized federated learning: An adversarial message-passing graph neural network approach,

    K. Li, Y . Liang, P. Li `o, W. Ni, F. Dressler, J. Crowcroft, and O. B. Akan, “User isolation poisoning on decentralized federated learning: An adversarial message-passing graph neural network approach,”IEEE Transactions on Neural Networks and Learning Systems, 2025

    work page 2025

  25. [25]

    Peft-as-an-attack! jailbreaking language models during federated parameter-efficient fine-tuning,

    S. Li, E. C.-H. Ngai, F. Ye, and T. V oigt, “Peft-as-an-attack! jailbreaking language models during federated parameter-efficient fine-tuning,”arXiv preprint arXiv:2411.19335, 2024

    work page arXiv 2024

  26. [26]

    Emerging safety attack and defense in federated instruction tuning of large language models,

    R. Ye, J. Chai, X. Liu, Y . Yang, Y . Wang, and S. Chen, “Emerging safety attack and defense in federated instruction tuning of large language models,”arXiv preprint arXiv:2406.10630, 2024

    work page arXiv 2024

  27. [27]

    Silent penetrator: Breaching cross-domain federated fine-tuning via feature shift-induced backdoor,

    W. Huang, G. Li, M. Chen, J. Li, and H. Zhu, “Silent penetrator: Breaching cross-domain federated fine-tuning via feature shift-induced backdoor,”IEEE Transactions on Information Forensics and Security, 2025

    work page 2025

  28. [28]

    Low rank comes with low security: Gradient assembly poisoning attacks against distributed lora-based llm systems,

    Y . Dong, M. Xu, Q. Hu, Y . Xiao, Q. Luo, Y . Zhang, Y . Zhang, and X. Cheng, “Low rank comes with low security: Gradient assembly poisoning attacks against distributed lora-based llm systems,”arXiv preprint arXiv:2601.00566, 2026

    work page arXiv 2026

  29. [29]

    A comprehensive survey of federated open-world learning,

    Z. Cai, J. Pang, Y . Li, Y . Huang, and Z. Xie, “A comprehensive survey of federated open-world learning,”IEEE Transactions on Network Science and Engineering, 2025

    work page 2025

  30. [30]

    Privacy and robustness in federated learning: Attacks and defenses,

    L. Lyu, H. Yu, X. Ma, C. Chen, L. Sun, J. Zhao, Q. Yang, and P. S. Yu, “Privacy and robustness in federated learning: Attacks and defenses,” IEEE transactions on neural networks and learning systems, vol. 35, no. 7, pp. 8726–8746, 2022

    work page 2022

  31. [31]

    Practical framework for privacy-preserving and byzantine-robust federated learning,

    B. Zhang, M. Fang, Z. Liu, B. Yi, P. Zhou, Y . Wang, T. Li, and Z. Liu, “Practical framework for privacy-preserving and byzantine-robust federated learning,”IEEE Transactions on Information Forensics and Security, vol. 21, pp. 61–75, 2025

    work page 2025

  32. [32]

    Overcoming noisy labels and non-iid data in edge federated learning,

    Y . Xu, Y . Liao, L. Wang, H. Xu, Z. Jiang, and W. Zhang, “Overcoming noisy labels and non-iid data in edge federated learning,”IEEE Trans- actions on Mobile Computing, vol. 23, no. 12, pp. 11 406–11 421, 2024

    work page 2024

  33. [33]

    Fedapm: Federated learning via admm with partial model personalization,

    S. Zhu, F. Nie, J. Zeng, S. Wang, Y . Sun, Y . Yao, S. Chen, Q. Xu, and C. Yang, “Fedapm: Federated learning via admm with partial model personalization,” inthe 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V . 2, 2025, pp. 4192–4202

    work page 2025

  34. [34]

    Efficient mobile-cloud collaborative aggregation for federated learning with la- tency resilience,

    W. Tang, J. Li, X. Zhang, Y . Miao, Z. Su, and R. H. Deng, “Efficient mobile-cloud collaborative aggregation for federated learning with la- tency resilience,”IEEE Transactions on Mobile Computing, 2025

    work page 2025

  35. [35]

    A ug fl: Aug- menting federated learning with pretrained models,

    S. Yue, Z. Qin, Y . Deng, J. Ren, Y . Zhang, and J. Zhang, “A ug fl: Aug- menting federated learning with pretrained models,”IEEE Transactions on Networking, 2025

    work page 2025

  36. [36]

    Snapcfl: A pre-clustering-based clustered federated learning framework for data and system heterogeneities,

    Y . Cheng, W. Zhang, Z. Zhang, J. Kang, Q. Xu, S. Wang, and D. Niyato, “Snapcfl: A pre-clustering-based clustered federated learning framework for data and system heterogeneities,”IEEE Transactions on Mobile Computing, vol. 24, no. 6, pp. 5214–5228, 2025

    work page 2025

  37. [37]

    Federal graph contrastive learning with secure cross-device validation,

    T. Wang, X. Zheng, J. Zhang, and L. Tian, “Federal graph contrastive learning with secure cross-device validation,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 14 145–14 158, 2024

    work page 2024

  38. [38]

    The autoencoding variational autoencoder,

    T. Cemgil, S. Ghaisas, K. Dvijotham, S. Gowal, and P. Kohli, “The autoencoding variational autoencoder,”Advances in Neural Information Processing Systems, vol. 33, pp. 15 077–15 087, 2020

    work page 2020

  39. [39]

    Biasing federated learning with a new adversarial graph attention network,

    K. Li, J. Zheng, W. Ni, H. Huang, P. Li `o, F. Dressler, and O. B. Akan, “Biasing federated learning with a new adversarial graph attention network,”IEEE Transactions on Mobile Computing, 2024

    work page 2024

  40. [40]

    Character-level convolutional net- works for text classification,

    X. Zhang, J. Zhao, and Y . LeCun, “Character-level convolutional net- works for text classification,”Advances in neural information processing systems, vol. 28, 2015

    work page 2015

  41. [41]

    DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

    V . Sanh, L. Debut, J. Chaumond, and T. Wolf, “Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter,”arXiv preprint arXiv:1910.01108, 2019

    work page internal anchor Pith review arXiv 1910

  42. [42]

    Pythia: A suite for analyzing large language models across training and scaling,

    S. Biderman, H. Schoelkopf, Q. G. Anthony, H. Bradley, K. O’Brien, E. Hallahan, M. A. Khan, S. Purohit, U. S. Prashanth, E. Raffet al., “Pythia: A suite for analyzing large language models across training and scaling,” inInternational Conference on Machine Learning. PMLR, 2023, pp. 2397–2430

    work page 2023

  43. [43]

    Qwen2.5-Coder Technical Report

    B. Hui, J. Yang, Z. Cui, J. Yang, D. Liu, L. Zhang, T. Liu, J. Zhang, B. Yu, K. Luet al., “Qwen2. 5-coder technical report,”arXiv preprint arXiv:2409.12186, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024