Results 21 to 25 of about 7,673 (25)

Towards Fair, Robust and Efficient Client Contribution Evaluation in Federated Learning [PDF]

arXiv
The performance of clients in Federated Learning (FL) can vary due to various reasons. Assessing the contributions of each client is crucial for client selection and compensation. It is challenging because clients often have non-independent and identically distributed (non-iid) data, leading to potentially noisy or divergent updates.
arxiv  

Client-supervised Federated Learning: Towards One-model-for-all Personalization [PDF]

arXiv
Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings. Most PerFL methods require extra learning processes on a client to adapt a globally shared model to the client-specific personalized model using its own local data.
arxiv  

Proving and Rewarding Client Diversity to Strengthen Resilience of Blockchain Networks [PDF]

arXiv
Client diversity in the Ethereum blockchain refers to the use of multiple independent implementations of the Ethereum protocol. This effectively enhances network resilience by reducing reliance on any single software client implementation. With client diversity, a single bug cannot tear the whole network down. However, despite multiple production-grade
arxiv  

Equilibria in Two-Stage Facility Location with Atomic Clients [PDF]

arXiv
We consider competitive facility location as a two-stage multi-agent system with two types of clients. For a given host graph with weighted clients on the vertices, first facility agents strategically select vertices for opening their facilities. Then, the clients strategically select which of the opened facilities in their neighborhood to patronize ...
arxiv  

FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification [PDF]

arXiv
Federated learning (FL) enables clients to collaboratively train machine learning models under the coordination of a server in a privacy-preserving manner. One of the main challenges in FL is that the server may not receive local updates from each client in each round due to client resource limitations and intermittent network connectivity.
arxiv