FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity

2024-04-15 · Kai Yi, Nidham Gazagnadou, Peter Richtárik, Lingjuan Lyu

The interest in federated learning has surged in recent research due to its unique ability to train a global model using privacy-secured information held locally on each client. This paper pays particular attention to the issue of client-side model heterogeneity, a pervasive challenge in the practical implementation of FL that escalates its complexity. Assuming a scenario where each client possesses varied memory storage, processing capabilities and network bandwidth - a phenomenon referred to as system heterogeneity - there is a pressing need to customize a unique model for each client. In response to this, we present an effective and adaptable federated framework FedP3, representing Federated Personalized and Privacy-friendly network Pruning, tailored for model heterogeneity scenarios. Our proposed methodology can incorporate and adapt well-established techniques to its specific instances. We offer a theoretical interpretation of FedP3 and its locally differential-private variant, DP-FedP3, and theoretically validate their efficiencies.

📄 PDF Abstract BibTeX arXiv:2404.09816

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning Network Pruning

Methods 이 논문이 사용한 방법론

Pruning 설명 없음

Exploring Federated Pruning for Large Language Models

2025-05-19 · Pengxin Guo, Yinong Wang, Wei Li, Mengting Liu 외

LLM pruning has emerged as a promising technology for compressing LLMs, enabling their deployment on resource-limited devices. However, current methodologies typically require access to public calibration samples, which …

Privacy Preserving

FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization

2025-04-01 · Haonan Wang, Zeli Liu, Kajimusugura Hoshino, Tuo Zhang 외

Federated Learning (FL) enables distributed training on edge devices but faces significant challenges due to resource constraints in edge environments, impacting both communication and computational efficiency. Existing …

Computational Efficiency Federated Learning

A federated graph neural network framework for privacy-preserving personalization

2022-06-02 · Nature Communications 2022 6 · Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Tao Qi 외

Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centra…

Federated Learning Graph Neural Network Privacy Preserving Recommendation Systems

FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence

2021-12-21 · Vasileios Perifanis, George Drosatos, Giorgos Stamatelatos, Pavlos S. Efraimidis

With the growing number of Location-Based Social Networks, privacy preserving location prediction has become a primary task for helping users discover new points-of-interest (POIs). Traditional systems consider a central…

Federated Learning Privacy Preserving Transfer Learning

MedFedPure: A Medical Federated Framework with MAE-based Detection and Diffusion Purification for Inference-Time Attacks

2025-11-07 · Mohammad Karami, Mohammad Reza Nemati, Aidin Kazemi, Ali Mikaeili Barzili 외 arxiv

Artificial intelligence (AI) has shown great potential in medical imaging, particularly for brain tumor detection using Magnetic Resonance Imaging (MRI). However, the models remain vulnerable at inference time when they …

Personalized Federated Learning Adversarial Robustness