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Found 6 papers, 4 papers with code
Flexible Clustered Federated Learning for Client-Level Data Distribution Shift
Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally.
CSAFL: A Clustered Semi-Asynchronous Federated Learning Framework
Asynchronous FL has a natural advantage in mitigating the straggler effect, but there are threats of model quality degradation and server crash.
FedSAE: A Novel Self-Adaptive Federated Learning Framework in Heterogeneous Systems
In our framework, the server evaluates devices' value of training based on their training loss.
FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup, in which we 1) group the training of clients based on the similarities between the clients' optimization directions for high training performance; 2) construct a new data-driven distance measure to improve the efficiency of the client clustering procedure.
A Generic Network Compression Framework for Sequential Recommender Systems
Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer.
Astraea: Self-balancing Federated Learning for Improving Classification Accuracy of Mobile Deep Learning Applications
However, unlike the common training dataset, the data distribution of the edge computing system is imbalanced which will introduce biases in the model training and cause a decrease in accuracy of federated learning applications.
