Cover Image

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Nan, Wang, Xiaolu, Du, Xiao, Cai, Puyu, Wang, Ting
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

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

Data Heterogeneity-Aware Client Selection for Federated Learning in Wireless Networks
by: Yang, Yanbing, et al.
Published: (2025)

Pollen: High-throughput Federated Learning Simulation via Resource-Aware Client Placement
by: Sani, Lorenzo, et al.
Published: (2023)

Dynamic Client Clustering, Bandwidth Allocation, and Workload Optimization for Semi-synchronous Federated Learning
by: Yu, Liangkun, et al.
Published: (2024)

Distributed Bilevel Optimization with Dual Pruning for Resource-limited Clients
by: Li, Mingyi, et al.
Published: (2025)

FlexFL: Heterogeneous Federated Learning via APoZ-Guided Flexible Pruning in Uncertain Scenarios
by: Chen, Zekai, et al.
Published: (2024)

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

Bridging Memory Gaps: Scaling Federated Learning for Heterogeneous Clients
by: Wu, Yebo, et al.
Published: (2024)

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

FedReMa: Improving Personalized Federated Learning via Leveraging the Most Relevant Clients
by: Liang, Han, et al.
Published: (2024)

One-Bit Model Aggregation for Differentially Private and Byzantine-Robust Personalized Federated Learning
by: Lan, Muhang, et al.
Published: (2025)

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

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

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

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

Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
by: Wang, Zijian, et al.
Published: (2025)

Memory-Efficient Federated Fine-Tuning of Large Language Models via Layer Pruning
by: Wu, Yebo, et al.
Published: (2025)

FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization
by: Ning, Zhiyuan, et al.
Published: (2024)

ClusterLess: Deadline-Aware Serverless Workflow Orchestration on Federated Edge Clusters
by: Farahani, Reza, et al.
Published: (2026)

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

Unity is Power: Semi-Asynchronous Collaborative Training of Large-Scale Models with Structured Pruning in Resource-Limited Clients
by: Li, Yan, et al.
Published: (2024)

Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients
by: Wu, Tian-Shuang, et al.
Published: (2026)

Heterogeneity-Aware Memory Efficient Federated Learning via Progressive Layer Freezing
by: Yebo, Wu, et al.
Published: (2024)

CA-AFP: Cluster-Aware Adaptive Federated Pruning
by: Jha, Om Govind, et al.
Published: (2026)

Rethinking Personalized Federated Learning with Clustering-based Dynamic Graph Propagation
by: Wang, Jiaqi, et al.
Published: (2024)

CHASe: Client Heterogeneity-Aware Data Selection for Effective Federated Active Learning
by: Zhang, Jun, et al.
Published: (2025)

FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning
by: QI, Jiaxing, et al.
Published: (2024)

Age-Aware Partial Gradient Update Strategy for Federated Learning Over the Air
by: Du, Ruihao, et al.
Published: (2025)

Optimization of Federated Learning's Client Selection for Non-IID Data Based on Grey Relational Analysis
by: Chen, Shuaijun, et al.
Published: (2023)

FedAPTA: Federated Multi-task Learning for Heterogeneous Devices with Adaptive Layer-wise Pruning and Task-aware Aggregation
by: Yu, Zhen, et al.
Published: (2025)

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

Embracing Federated Learning: Enabling Weak Client Participation via Partial Model Training
by: Lee, Sunwoo, et al.
Published: (2024)

EAT: QoS-Aware Edge-Collaborative AIGC Task Scheduling via Attention-Guided Diffusion Reinforcement Learning
by: Xu, Zhifei, et al.
Published: (2025)

When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
by: Zhang, Pengyu, et al.
Published: (2024)

CLIP: Client-Side Invariant Pruning for Mitigating Stragglers in Secure Federated Learning
by: DiMaggio, Anthony, et al.
Published: (2025)

Hypernetworks for Model-Heterogeneous Personalized Federated Learning
by: Zhang, Chen, et al.
Published: (2025)

Decentralized Personalization for Federated Medical Image Segmentation via Gossip Contrastive Mutual Learning
by: Chen, Jingyun, et al.
Published: (2025)

Personalized Federated Learning via ADMM with Moreau Envelope
by: Zhu, Shengkun, et al.
Published: (2023)

FedCache: A Knowledge Cache-driven Federated Learning Architecture for Personalized Edge Intelligence
by: Wu, Zhiyuan, et al.
Published: (2023)

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