IEEE

Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks With Differential Privacy

Abstract: As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain ...
Microsoft

Learning to Retrieve Iteratively for In-Context Learning

We introduce iterative retrieval, a novel framework that empowers retrievers to make iterative decisions through policy optimization. Finding an optimal portfolio of retrieved items is a combinatorial ...
IEEE

Optimal Model Transfer and Dynamic Parameter Server Selection for Efficient Federated Learning in IoT-Edge Systems With Non-IID Data

Abstract: Distributed and privacy-preserving federated learning (FL) has been associated with edge computing systems for developing intelligent IoT applications. However, collecting data individually ...