arXiv Machine Learning
Agentic Federated Learning: Rule-Based Client and Server Agents for Adaptive Training
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 5
FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare
arXiv:2608. 03498v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data.
arXiv Machine Learning
1d ago
Latent Information Sharing for Accelerating Federated Learning
The paper introduces a latent information sharing scheme for federated learning that mitigates client drift by sharing a small amount of hidden‑layer activations. The authors demonstrate both theoretically and empirically that this approach improves training efficiency while maintaining convergence guarantees and data privacy. Compared to existing methods such as FedProx, SCAFFOLD, FedPVR, FedProto, and SplitFed, the proposed method achieves higher model accuracy within a fixed round budget without adding significant communication overhead.
Hugging Face Trending Papers
Aug 4
FedCARE: A Multi-Objective Personalised Federated Learning Framework for Smart Healthcare
Federated Learning (FL) enables collaborative model training across distributed healthcare institutions without centralising sensitive patient data. However, real-world healthcare federations are often characterised not only by non-IID data, but also by heterogeneous clinical objectives and partially overlapping feature spaces.
arXiv Machine Learning
Jul 21
Online-Score-Aided Federated Learning for Resource-Constrained Wireless Clients with Continual Data Arrival
arXiv:2408. 05886v5 Announce Type: replace Abstract: Heterogeneous system configurations of distributed clients connected to the central server (CS) via a time-varying wireless network pose significant challenges for popular distributed machine learning (ML) algorithms such as federated learning (FL).
arXiv AI
Aug 10
FedLBW: A Loss-Based Weighting Strategy for Federated Learning on Non-IID Data in Wireless Networks
arXiv:2608. 07007v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative machine learning (ML) across distributed clients while preserving privacy.
arXiv Machine Learning
Aug 4
Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learning
arXiv:2608. 02250v1 Announce Type: new Abstract: Federated learning (FL) is a popular distributed learning framework where multiple clients perform local training and a server aggregates the locally updated models.