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Enhancing Data Quality in Federated Fine-Tuning of Foundation Models
In the current landscape of foundation model training, there is a significant reliance on public domain data, which is nearing exhaustion according to recent research.
FedAnchor: Enhancing Federated Semi-Supervised Learning with Label Contrastive Loss for Unlabeled Clients
Federated learning (FL) is a distributed learning paradigm that facilitates collaborative training of a shared global model across devices while keeping data localized.
Secure Vertical Federated Learning Under Unreliable Connectivity
Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models independently.
Efficient Vertical Federated Learning with Secure Aggregation
The majority of work in privacy-preserving federated learning (FL) has been focusing on horizontally partitioned datasets where clients share the same sets of features and can train complete models independently.
Protea: Client Profiling within Federated Systems using Flower
Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users.
