mlsanityData to 7 Oct 2026

Influence-Oriented Personalized Federated Learning

arXiv 2410.03315 ↗First posted 4 Oct 2024cs.LG cs.AI cs.DCcited by 6

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Abstract (arXiv)

Federated learning (FL) is a machine learning paradigm where clients with different behaviors and preferences can learn collaboratively without compromising data privacy. Typical FL methods often rely on fixed weighting for parameter aggregation, thereby neglecting the mutual influence among clients. In practice, clients with similar preferences or backgrounds may provide more useful knowledge to each other, which can be leveraged to improve local performance. However, how to quantify such cross-client influence and how to exploit it for personalized aggregation remain underexplored. To address this gap, we propose an influence-oriented Federated learning framework which quantitatively measures Client-level and Class-level Influence to realize adaptive parameter aggregation for each client (FedC^2I for short). Our core idea is to explicitly model the inter-client influence within an FL system via the well-crafted influence vector and influence matrix. Specifically, FedC^2I incorporate influence vectors to quantify client-level influence, enables clients to selectively acquire knowledge from others, and guides the aggregation of feature representation layers. Meanwhile, the influence matrix captures class-level influence in a more fine-grained manner to achieve personalized classifier aggregation. We evaluate the performance of FedC^2I against existing federated learning methods under non-IID settings, and the results demonstrate the superiority of our method in terms of effectiveness, robustness, and interpretability.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 38 of its 56 references that are in the map. First posted on arXiv 4 Oct 2024.

Builds on

38 of 56 references.

References · 38

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26 more
  1. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
  2. 2019Federated Learning with Personalization Layerscited by 533
  3. 2020An Efficient Framework for Clustered Federated Learningcited by 505
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026Towards Anomaly Detection on Relational Datacited by 4
  2. 2026TRE: Training-Free Hallucination Detection for Diffusion Language Modelscited by 0
  3. 2026PaSta: Noisy Node Classification with Partial Label Learningcited by 0
  4. 2026SIM: Subspace Interaction-based Method for Token-Level Text Anomaly Detectioncited by 0
  5. 2026HOPE: Heterophily-Aware Open-Set Node Classification with Pseudo-Extrapolationcited by 0
  6. 2026DiFA: Dual Evidence Fusion and Aggregation for Token-Level Text Anomaly Detectioncited by 0