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Personalized Federated Learning
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The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency. Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive Mapping
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions
Personalized Federated Learning under Model Dissimilarity Constraints
Incentivizing Inclusive Contributions in Model Sharing Markets
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
Privacy-Preserving Personalized Federated Learning for Distributed Photovoltaic Disaggregation under Statistical Heterogeneity
DP2FL: Dual Prompt Personalized Federated Learning in Foundation Models
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss
Exploring Personalized Federated Learning Architectures for Violence Detection in Surveillance Videos
RCC-PFL: Robust Client Clustering under Noisy Labels in Personalized Federated Learning
pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning
Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning
Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates
Mitigating Membership Inference Vulnerability in Personalized Federated Learning
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning
Personalized Federated Learning via Learning Dynamic Graphs
WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models
Asynchronous Personalized Federated Learning through Global Memorization
Personalized Federated Learning for Egocentric Video Gaze Estimation with Comprehensive Parameter Frezzing
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data
PFedDST: Personalized Federated Learning with Decentralized Selection Training
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated Learning
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization
SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning
Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning
Personalized Federated Learning for Cellular VR: Online Learning and Dynamic Caching
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.