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Found 8 papers, 8 papers with code
An Upload-Efficient Scheme for Transferring Knowledge From a Server-Side Pre-trained Generator to Clients in Heterogeneous Federated Learning
Heterogeneous Federated Learning (HtFL) enables collaborative learning on multiple clients with different model architectures while preserving privacy.
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning
To reduce the high communication cost of transmitting model parameters, a major challenge in HtFL, prototype-based HtFL methods are proposed to solely share class representatives, a. k. a, prototypes, among heterogeneous clients while maintaining the privacy of clients' models.
PFLlib: Personalized Federated Learning Algorithm Library
Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm that allows collaborative learning with data privacy protection, personalized FL (pFL) has gained significant prominence as a research direction within the FL domain.
Eliminating Domain Bias for Federated Learning in Representation Space
Recently, federated learning (FL) is popular for its privacy-preserving and collaborative learning abilities.
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Federated Learning (FL) is popular for its privacy-preserving and collaborative learning capabilities.
FedCP: Separating Feature Information for Personalized Federated Learning via Conditional Policy
To address this, we propose the Federated Conditional Policy (FedCP) method, which generates a conditional policy for each sample to separate the global information and personalized information in its features and then processes them by a global head and a personalized head, respectively.
FedALA: Adaptive Local Aggregation for Personalized Federated Learning
A key challenge in federated learning (FL) is the statistical heterogeneity that impairs the generalization of the global model on each client.
TLSAN: Time-aware Long- and Short-term Attention Network for Next-item Recommendation
Especially, the attention mechanism in deep learning enables recommender systems to incorporate various features in an adaptive way.
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