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Found 5 papers, 1 papers with code
FedNC: A Secure and Efficient Federated Learning Method with Network Coding
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency.
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning
By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models.
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
Then, we formulate the problem of selecting optimal IS weights and obtain the theoretical solutions.
Federated Multi-Agent Actor-Critic Learning for Age Sensitive Mobile Edge Computing
To the best of our knowledge, it's the first joint MEC collaboration algorithm that combines the edge federated mode with the multi-agent actor-critic reinforcement learning.
Machine Learning Based Prediction and Classification of Computational Jobs in Cloud Computing Centers
With the rapid growth of the data volume and the fast increasing of the computational model complexity in the scenario of cloud computing, it becomes an important topic that how to handle users' requests by scheduling computational jobs and assigning the resources in data center.
