Cover Image

Personalized Federated Learning for Generative AI-Assisted Semantic Communications

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Peng, Yubo, Jiang, Feibo, Dong, Li, Wang, Kezhi, Yang, Kun
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Distributed, Parallel, and Cluster Computing Information Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2026)

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

Hierarchical Federated Learning with SignSGD: A Highly Communication-Efficient Approach
by: Kazemi, Amirreza, et al.
Published: (2026)

Closing the Generalization Gap in Parameter-efficient Federated Edge Learning
by: Du, Xinnong, et al.
Published: (2025)

Beyond Trade-offs: A Unified Framework for Privacy, Robustness, and Communication Efficiency in Federated Learning
by: Xia, Yue, et al.
Published: (2025)

CAFe: Cost and Age aware Federated Learning
by: Liyanaarachchi, Sahan, et al.
Published: (2024)

Private Aggregation for Byzantine-Resilient Heterogeneous Federated Learning
by: Egger, Maximilian, et al.
Published: (2025)

Scalable and Reliable Over-the-Air Federated Edge Learning
by: Egger, Maximilian, et al.
Published: (2024)

Perfect Privacy for Discriminator-Based Byzantine-Resilient Federated Learning
by: Xia, Yue, et al.
Published: (2025)

BICompFL: Stochastic Federated Learning with Bi-Directional Compression
by: Egger, Maximilian, et al.
Published: (2025)

Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning
by: Yan, Jintao, et al.
Published: (2024)

LoByITFL: Low Communication Secure and Private Federated Learning
by: Xia, Yue, et al.
Published: (2024)

Satellite Federated Edge Learning: Architecture Design and Convergence Analysis
by: Shi, Yuanming, et al.
Published: (2024)

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

Robust Regression with Ensembles Communicating over Noisy Channels
by: Ben-Hur, Yuval, et al.
Published: (2024)

Personalized Interpretation on Federated Learning: A Virtual Concepts approach
by: Yan, Peng, et al.
Published: (2024)

GAI-Enabled Explainable Personalized Federated Semi-Supervised Learning
by: Peng, Yubo, et al.
Published: (2024)

A Unified Analysis of Federated Learning with Arbitrary Client Participation
by: Wang, Shiqiang, et al.
Published: (2022)

Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm
by: Lyu, Zhonghao, et al.
Published: (2024)

FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated Learning
by: Yi, Liping, et al.
Published: (2023)

Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees
by: Yu, Xin, et al.
Published: (2024)

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

The Entrapment Problem in Random Walk Decentralized Learning
by: Liu, Zonghong, et al.
Published: (2024)

Learning from Acceptance: Cumulative Regret in the Game of Coding
by: Nodehi, Hanzaleh Akbari, et al.
Published: (2026)

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

Coded Computing for Resilient Distributed Computing: A Learning-Theoretic Framework
by: Moradi, Parsa, et al.
Published: (2024)

MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes
by: Li, Xin-Chun, et al.
Published: (2024)

Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations
by: Edin, Adrian, et al.
Published: (2026)

SLO-Aware Compute Resource Allocation for Prefill-Decode Disaggregated LLM Inference
by: Li, Luchang, et al.
Published: (2026)

A Generalized Hierarchical Federated Learning Framework with Theoretical Guarantees
by: Azimi-Abarghouyi, Seyed Mohammad, et al.
Published: (2025)

Federated One-Shot Learning with Data Privacy and Objective-Hiding
by: Egger, Maximilian, et al.
Published: (2025)

Privacy-aware Berrut Approximated Coded Computing for Federated Learning
by: Luaña, Xavier Martínez, et al.
Published: (2024)

WW-FL: Secure and Private Large-Scale Federated Learning
by: Marx, Felix, et al.
Published: (2023)

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization
by: Li, Zhe, et al.
Published: (2024)

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

Byzantine-Resilient Secure Aggregation for Federated Learning Without Privacy Compromises
by: Xia, Yue, et al.
Published: (2024)

Scheduling and Aggregation Design for Asynchronous Federated Learning over Wireless Networks
by: Hu, Chung-Hsuan, et al.
Published: (2022)

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

Approximate Gradient Coding for Privacy-Flexible Federated Learning with Non-IID Data
by: Makkonen, Okko, et al.
Published: (2024)

A New Theoretical Perspective on Data Heterogeneity in Federated Optimization
by: Wang, Jiayi, et al.
Published: (2024)