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Latent Information Sharing for Accelerating Federated Learning

arXiv 2610.01126 ↗First posted 1 Oct 2026cs.LGcited by 0

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Abstract (arXiv)

Federated learning (FL) is a communication-efficient distributed learning paradigm. However, client drift remains one of the most critical challenges, hindering the efficient training of a global model. In this study, we propose a novel latent information sharing scheme that directly mitigates data heterogeneity across clients. Our theoretical and empirical results show that sharing a small amount of hidden-layer activations significantly improves training efficiency while preserving convergence guarantees and data privacy. Furthermore, we compare our method with existing FL approaches designed to address client drift, including FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, and demonstrate superior model accuracy under a fixed round budget without incurring excessive communication overhead. Overall, this work presents a promising new knowledge aggregation scheme and provides a comprehensive analysis of the impact of activation sharing on federated optimization.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 27 references that are in the map. First posted on arXiv 1 Oct 2026.

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References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  5. 2020Adaptive Federated Optimizationcited by 1,046
  6. 2018LEAF: A Benchmark for Federated Settingscited by 792
  7. 2021Model-Contrastive Federated Learningcited by 766
  8. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  9. 2018Certified Robustness to Adversarial Examples with Differential Privacycited by 468
  10. 2014Understanding Deep Image Representations by Inverting Themcited by 403
  11. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 395
  12. 2021FedProto: Federated Prototype Learning across Heterogeneous Clientscited by 352
6 more
  1. 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
  2. 2019The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networkscited by 246
  3. 2022FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learningcited by 64
  4. 2021Layer-wise Adaptive Model Aggregation for Scalable Federated Learningcited by 36
  5. 2024FedImpro: Measuring and Improving Client Update in Federated Learningcited by 15
  6. 2025Generative Model Inversion Through the Lens of the Manifold Hypothesiscited by 1

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