Buchumschlag

Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Zhao, Xiyu, Cui, Qimei, Li, Weicai, Ni, Wei, Hossain, Ekram, Sheng, Quan Z., Tao, Xiaofeng, Zhang, Ping
Format:	Preprint
Veröffentlicht:	2025
Schlagworte:	Machine Learning Distributed, Parallel, and Cluster Computing
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
von: Zhao, Xiyu, et al.
Veröffentlicht: (2025)

A Novel Indicator for Quantifying and Minimizing Information Utility Loss of Robot Teams
von: Zhao, Xiyu, et al.
Veröffentlicht: (2025)

Decentralized Federated Learning Over Imperfect Communication Channels
von: Li, Weicai, et al.
Veröffentlicht: (2024)

Route-and-Aggregate Decentralized Federated Learning Under Communication Errors
von: Li, Weicai, et al.
Veröffentlicht: (2025)

Decentralizing Trust: Consortium Blockchains and Hyperledger Fabric Explained
von: Vera-Rivera, Angelo, et al.
Veröffentlicht: (2025)

FourierCompress: Layer-Aware Spectral Activation Compression for Efficient and Accurate Collaborative LLM Inference
von: Ma, Jian, et al.
Veröffentlicht: (2025)

FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs
von: Zhou, Zihao, et al.
Veröffentlicht: (2025)

Communication-and-Computation Efficient Split Federated Learning: Gradient Aggregation and Resource Management
von: Liang, Yipeng, et al.
Veröffentlicht: (2025)

Federated Learning with Integrated Sensing, Communication, and Computation: Frameworks and Performance Analysis
von: Liang, Yipeng, et al.
Veröffentlicht: (2024)

Drift-Aware Federated Learning: A Causal Perspective
von: Fang, Yunjie, et al.
Veröffentlicht: (2025)

Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification
von: Papanikolaou, Athanasios, et al.
Veröffentlicht: (2026)

ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains
von: Zhao, Chonghe, et al.
Veröffentlicht: (2025)

Empirical Analysis of Asynchronous Federated Learning on Heterogeneous Devices: Efficiency, Fairness, and Privacy Trade-offs
von: Mohammadi, Samaneh, et al.
Veröffentlicht: (2025)

Megha: Decentralized Global Fair Scheduling for Federated Clusters
von: Thiyyakat, Meghana, et al.
Veröffentlicht: (2021)

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

Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning
von: Xiao, Bingnan, et al.
Veröffentlicht: (2026)

CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models
von: Yi, Mengjun, et al.
Veröffentlicht: (2025)

PPVF: An Efficient Privacy-Preserving Online Video Fetching Framework with Correlated Differential Privacy
von: Zhang, Xianzhi, et al.
Veröffentlicht: (2024)

ALANINE: A Novel Decentralized Personalized Federated Learning For Heterogeneous LEO Satellite Constellation
von: Zhao, Liang, et al.
Veröffentlicht: (2024)

On Fair Ordering and Differential Privacy
von: Cohen, Shir, et al.
Veröffentlicht: (2025)

Privacy-Preserving Federated Heavy Hitter Analytics for Non-IID Data
von: Shao, Jiaqi, et al.
Veröffentlicht: (2023)

Evolving Topics in Federated Learning: Trends, and Emerging Directions for IS
von: Uddin, Md Raihan, et al.
Veröffentlicht: (2024)

Privacy-preserving Chunk Scheduling in a BitTorrent Implementation of Federated Learning
von: Li, Naicheng, et al.
Veröffentlicht: (2026)

FEDBUD: Joint Incentive and Privacy Optimization for Resource-Constrained Federated Learning
von: Liu, Tao, et al.
Veröffentlicht: (2026)

Personalized Federated Learning on Data with Dynamic Heterogeneity under Limited Storage
von: Tan, Sixing, et al.
Veröffentlicht: (2024)

Wait or Not to Wait: Evaluating Trade-Offs between Speed and Precision in Blockchain-based Federated Aggregation
von: Nguyen, Huong, et al.
Veröffentlicht: (2024)

Experiences Building Enterprise-Level Privacy-Preserving Federated Learning to Power AI for Science
von: Li, Zilinghan, et al.
Veröffentlicht: (2025)

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

To Compress or Not To Compress: Energy Trade-Offs and Benefits of Lossy Compressed I/O
von: Wilkins, Grant, et al.
Veröffentlicht: (2024)

Breaking the Aggregation Bottleneck in Federated Recommendation: A Personalized Model Merging Approach
von: Chen, Jundong, et al.
Veröffentlicht: (2025)

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

EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control
von: Li, Xiaochen, et al.
Veröffentlicht: (2024)

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

SPES: Towards Optimizing Performance-Resource Trade-Off for Serverless Functions
von: Lee, Cheryl, et al.
Veröffentlicht: (2024)

GoodSpeed: Optimizing Fair Goodput with Adaptive Speculative Decoding in Distributed Edge Inference
von: Tran, Phuong, et al.
Veröffentlicht: (2025)

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

MoA-Off: Adaptive Heterogeneous Modality-Aware Offloading with Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
von: Yang, Zheming, et al.
Veröffentlicht: (2025)

Emulating Full Participation: An Effective and Fair Client Selection Strategy for Federated Learning
von: Li, Qingming, et al.
Veröffentlicht: (2024)

pFedLVM: A Large Vision Model (LVM)-Driven and Latent Feature-Based Personalized Federated Learning Framework in Autonomous Driving
von: Kou, Wei-Bin, et al.
Veröffentlicht: (2024)

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning
von: Hosain, Md. Tanzib, et al.
Veröffentlicht: (2025)