Aug 30, 2023 · Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing ...

In this paper, we proposed a novel federated representation learning framework, called FedCiR, to solve the feature- skewed issue in FL. Based on our ...

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without sharing their raw data.

Oct 22, 2024 · A client-invariant representation learning framework that enables clients to extract informative and client-invariant features.

Dive into the research topics of 'FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features'. Together they form a unique fingerprint.

Federated learning (FL) is a distributed learning paradigm that maximizes the potential of data-driven models for edge devices without ...

A survey of what to share in federated learning: perspectives on ... 2024. Fedcir: Client-invariant representation learning for federated non-iid features.

Fedcir: Client-invariant representation learning for federated non-iid features. Z Li, Z Lin, J Shao, Y Mao, J Zhang. IEEE Transactions on Mobile Computing ...

New [03/2024] Paper “FedCiR: Client-invariant representation learning for federated non-IID features” was accepted by IEEE Transactions on Mobile Computing (TMC) ...

FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features ... Feature Matching Data Synthesis for Non-IID Federated Learning · no code ...