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Found 5 papers, 3 papers with code
Accelerated Training on Low-Power Edge Devices
To accelerate training, we propose to jointly adjust the system and application parameters (in our case, the GPU frequency and the batch size of the training task) while adhering to the power constraints on devices.
Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments
Federated Learning (FL) is an emerging machine learning paradigm that enables the collaborative training of a shared global model across distributed clients while keeping the data decentralized.
Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning
To address these challenges and enable heterogeneous client models in serverless FL, we utilize Knowledge Distillation (KD) in this paper.
Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing
The size of the observed neighborhood limits the generalizability to different graphs and affects the reactivity of agents, the quality of the selected actions, and the communication overhead.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
FedLesScan: Mitigating Stragglers in Serverless Federated Learning
We implement our strategy by extending an open-source serverless FL system called FedLess.
