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Found 11 papers, 1 papers with code
Computation and Communication Efficient Lightweighting Vertical Federated Learning
Moreover, we establish a convergence bound for our LVFL algorithm, which accounts for both communication and computational lightweighting ratios.
Taming Cross-Domain Representation Variance in Federated Prototype Learning with Heterogeneous Data Domains
Federated learning (FL) allows collaborative machine learning training without sharing private data.
FedMM: Federated Multi-Modal Learning with Modality Heterogeneity in Computational Pathology
The fusion of complementary multimodal information is crucial in computational pathology for accurate diagnostics.
Federated Learning with Instance-Dependent Noisy Label
We introduce a novel algorithm called FedBeat (Federated Learning with Bayesian Ensemble-Assisted Transition Matrix Estimation).
CAFE: Carbon-Aware Federated Learning in Geographically Distributed Data Centers
Training large-scale artificial intelligence (AI) models demands significant computational power and energy, leading to increased carbon footprint with potential environmental repercussions.
Joint Client Assignment and UAV Route Planning for Indirect-Communication Federated Learning
The use of indirect communication presents new challenges for convergence analysis and optimization, as the delay introduced by the transporters' movement creates issues for both global model dissemination and local model collection.
Accelerating Hybrid Federated Learning Convergence under Partial Participation
In this paper, we provide theoretical analysis of hybrid FL under clients' partial participation to validate that partial participation is the key constraint on convergence speed.
On the Local Cache Update Rules in Streaming Federated Learning
In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity.
Federated Learning via Indirect Server-Client Communications
In this paper, we propose a novel FL framework, named FedEx (short for FL via Model Express Delivery), that utilizes mobile transporters (e. g., Unmanned Aerial Vehicles) to establish indirect communication channels between the server and the clients.
Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying
To address this issue, the paper explores the impact of mobility on the convergence performance of asynchronous FL.
FedSEAL: Semi-Supervised Federated Learning with Self-Ensemble Learning and Negative Learning
Federated learning (FL), a popular decentralized and privacy-preserving machine learning (FL) framework, has received extensive research attention in recent years.
