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Found 5 papers, 1 papers with code
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
The proposed method utilizes efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i. i. d.
Sim-to-Real Transfer in Multi-agent Reinforcement Networking for Federated Edge Computing
Federated Learning (FL) over wireless multi-hop edge computing networks, i. e., multi-hop FL, is a cost-effective distributed on-device deep learning paradigm.
EdgeML: Towards Network-Accelerated Federated Learning over Wireless Edge
To solve such MDP, multi-agent reinforcement learning (MA-RL) algorithms along with domain-specific action space refining schemes are developed, which online learn the delay-minimum forwarding paths to minimize the model exchange latency between the edge devices (i. e., workers) and the remote server.
Diverse Client Selection for Federated Learning via Submodular Maximization
In every communication round of federated learning, a random subset of clients communicate their model updates back to the server which then aggregates them all.
MutualNet: Adaptive ConvNet via Mutual Learning from Different Model Configurations
MutualNet is a general training methodology that can be applied to various network structures (e. g., 2D networks: MobileNets, ResNet, 3D networks: SlowFast, X3D) and various tasks (e. g., image classification, object detection, segmentation, and action recognition), and is demonstrated to achieve consistent improvements on a variety of datasets.
