Cover Image

CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wang, Mengdi, Bozkir, Efe, Kasneci, Enkelejda
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TurboSVM-FL: Boosting Federated Learning through SVM Aggregation for Lazy Clients
by: Wang, Mengdi, et al.
Published: (2024)

Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation
by: Yang, Haibo, et al.
Published: (2024)

MP-SL: Multihop Parallel Split Learning
by: Tirana, Joana, et al.
Published: (2024)

Towards Client Driven Federated Learning
by: Li, Songze, et al.
Published: (2024)

Communication-Efficient Federated AUC Maximization with Cyclic Client Participation
by: Vangapally, Umesh, et al.
Published: (2026)

MimiC: Combating Client Dropouts in Federated Learning by Mimicking Central Updates
by: Sun, Yuchang, et al.
Published: (2023)

FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
by: Jiang, Chutian, et al.
Published: (2024)

Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification
by: Jalali, Hamed, et al.
Published: (2022)

Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
by: Zuo, Yuncong, et al.
Published: (2024)

Federated Graph Learning with Graphless Clients
by: Fu, Xingbo, et al.
Published: (2024)

FedClust: Optimizing Federated Learning on Non-IID Data through Weight-Driven Client Clustering
by: Islam, Md Sirajul, et al.
Published: (2024)

FedImpro: Measuring and Improving Client Update in Federated Learning
by: Tang, Zhenheng, et al.
Published: (2024)

Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data
by: Liu, Ji, et al.
Published: (2024)

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients
by: Wang, Xiaolu, et al.
Published: (2024)

Federated Learning in the Presence of Adversarial Client Unavailability
by: Su, Lili, et al.
Published: (2023)

Debiasing Federated Learning with Correlated Client Participation
by: Sun, Zhenyu, et al.
Published: (2024)

Local Gradient Regulation Stabilizes Federated Learning under Client Heterogeneity
by: Luo, Ping, et al.
Published: (2026)

Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
by: Yuan, Liangqi, et al.
Published: (2024)

High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates
by: Lu, Fred, et al.
Published: (2024)

SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
by: Rieger, Phillip, et al.
Published: (2025)

Ranking-based Client Selection with Imitation Learning for Efficient Federated Learning
by: Tian, Chunlin, et al.
Published: (2024)

Robust Federated Learning in Unreliable Wireless Networks: A Client Selection Approach
by: Wang, Yanmeng, et al.
Published: (2025)

Communication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
by: Tsouvalas, Vasileios, et al.
Published: (2024)

Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
by: Balivada, Nihal, et al.
Published: (2026)

Greedy Shapley Client Selection for Communication-Efficient Federated Learning
by: Singhal, Pranava, et al.
Published: (2023)

Maverick-Aware Shapley Valuation for Client Selection in Federated Learning
by: Yang, Mengwei, et al.
Published: (2024)

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning
by: Dhasade, Akash, et al.
Published: (2024)

Adaptive Client Selection with Personalization for Communication Efficient Federated Learning
by: de Souza, Allan M., et al.
Published: (2024)

DiSCo: Device-Server Collaborative LLM-Based Text Streaming Services
by: Sun, Ting, et al.
Published: (2025)

The Diversity Bonus: Learning from Dissimilar Distributed Clients in Personalized Federated Learning
by: Wu, Xinghao, et al.
Published: (2024)

Enhancing Split Learning with Sharded and Blockchain-Enabled SplitFed Approaches
by: Sokhankhosh, Amirreza, et al.
Published: (2025)

Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers
by: Paramanayakam, Varatheepan, et al.
Published: (2025)

P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
by: Fan, Wei, et al.
Published: (2025)

S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
by: Sánchez, Pedro Miguel Sánchez, et al.
Published: (2025)

SplitFT: An Adaptive Federated Split Learning System For LLMs Fine-Tuning
by: Shan, Yimeng, et al.
Published: (2026)

Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
by: Fenoglio, Dario, et al.
Published: (2024)

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
by: Duan, Moming, et al.
Published: (2021)

Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
by: Ma, Mengmeng, et al.
Published: (2024)

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning
by: Li, Nan, et al.
Published: (2025)

Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
by: Xiong, Guojun, et al.
Published: (2023)