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Personalized Federated Learning

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하위 태스크: 0
평가표: 7
보존된 평가 기록: 24

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.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/russelljeffrey/TPFL

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TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering

https://github.com/s-huu/pmtl

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Private Multi-Task Learning: Formulation and Applications to Federated Learning

https://github.com/sajjad-ucsb/pfedmma

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pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Models

https://github.com/shawnblyu/personalized-federated-learning-with-multiple-known-clusters

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Personalized Federated Learning with Multiple Known Clusters

https://github.com/shenzebang/SRPFL

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Straggler-Resilient Personalized Federated Learning

https://github.com/skyofbeginning/fedmgp

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Personalized Federated Continual Learning via Multi-granularity Prompt

https://github.com/sourasb05/PerMFL_1

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Personalized Multi-tier Federated Learning

https://github.com/sustainable-computing/blinder

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Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated Learning

https://github.com/tj-kim/pfeddef_v1

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Characterizing Internal Evasion Attacks in Federated Learning

https://github.com/tl-system/plato

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Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning

https://github.com/tsingz0/fedcp

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FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy

https://github.com/vaseline555/superfed

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Connecting Low-Loss Subspace for Personalized Federated Learning

https://github.com/vectorinstitute/fl4health

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A Comprehensive View of Personalized Federated Learning on Heterogeneous Clinical Datasets

https://github.com/vityavitalich/meritfed

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Low-Resource Machine Translation through the Lens of Personalized Federated Learning

https://github.com/vityavitalich/meritopt

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Low-Resource Machine Translation through the Lens of Personalized Federated Learning

https://github.com/wph6/fhbench

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FHBench: Towards Efficient and Personalized Federated Learning for Multimodal Healthcare

https://github.com/wzjialang/pfedsis

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Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery

https://github.com/xiaoni-61/dh-benchmark

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Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning

https://github.com/xinghaowu/feddecomp

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Decoupling General and Personalized Knowledge in Federated Learning via Additive and Low-Rank Decomposition

https://github.com/xztcwang/fedmn

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Personalized Federated Learning via Heterogeneous Modular Networks

https://github.com/yiyuec/fed-qssl

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Fed-QSSL: A Framework for Personalized Federated Learning under Bitwidth and Data Heterogeneity

https://github.com/yutong-dai/fednh

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Tackling Data Heterogeneity in Federated Learning with Class Prototypes

https://github.com/yxdyc/pfedgate

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Efficient Personalized Federated Learning via Sparse Model-Adaptation

https://github.com/zackzikaixiao/fedloge

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FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data

https://github.com/zhouyucs/btfl

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BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learning

https://github.com/zhyczy/fed-co2

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Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning

https://github.com/zhyczy/fedtp

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FedTP: Federated Learning by Transformer Personalization

https://github.com/zi-yuanyang/hyperfed

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Hypernetwork-based Personalized Federated Learning for Multi-Institutional CT Imaging

https://github.com/ziruniu0/fedspu

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FedSPU: Personalized Federated Learning for Resource-constrained Devices with Stochastic Parameter Update

https://github.com/zshuai8/FedGMM_ICML2023

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Personalized Federated Learning under Mixture of Distributions

https://github.com/CharlieDinh/FedU_FMTL

A New Look and Convergence Rate of Federated Multi-Task Learning with Laplacian Regularization

https://github.com/KarhouTam/FL-bench

Adaptive Personalized Federated Learning

https://github.com/RuiLiu-cc/FLEEG

Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning

https://github.com/TsingZ0/FL-IoT

Adaptive Personalized Federated Learning

https://github.com/TsingZ0/PFL-Non-IID

Adaptive Personalized Federated Learning

https://github.com/ZLHe0/fedprox-bilevel

Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees

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