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Found 7 papers, 5 papers with code
Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives
To fully unleash the potential of FL, we advocate rethinking the design of current FL frameworks and extending it to a more generalized concept: Open Federated Learning Platforms, positioned as a crowdsourcing collaborative machine learning infrastructure for all Internet users.
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market
The emerging availability of trained machine learning models has put forward the novel concept of Machine Learning Model Market in which one can harness the collective intelligence of multiple well-trained models to improve the performance of the resultant model through one-shot federated learning and ensemble learning in a data-free manner.
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.
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.
