[HTML][HTML] Model aggregation techniques in federated learning: A comprehensive survey
Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …
models to be trained on client devices while ensuring the privacy of user data. Model …
Client selection in federated learning: Principles, challenges, and opportunities
As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …
learning (FL) has received tremendous attention from both industry and academia. In a …
Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …
increasingly appealing to exploit distributed data communication and learning. Specifically …
Cooperative and competitive multi-agent systems: From optimization to games
Multi-agent systems can solve scientific issues related to complex systems that are difficult or
impossible for a single agent to solve through mutual collaboration and cooperation …
impossible for a single agent to solve through mutual collaboration and cooperation …
Device sampling for heterogeneous federated learning: Theory, algorithms, and implementation
The conventional federated learning (FedL) architecture distributes machine learning (ML)
across worker devices by having them train local models that are periodically aggregated by …
across worker devices by having them train local models that are periodically aggregated by …