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Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data
The remaining cluster with surviving clients is then used for training the best FL model (i. e., remaining FL model).
Budgeted Online Selection of Candidate IoT Clients to Participate in Federated Learning
In this work, we solve the problem of optimizing accuracy in stateful FL with a budgeted number of candidate clients by selecting the best candidate clients in terms of test accuracy to participate in the training process.
Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services
With Machine Learning (ML) services now used in a number of mission-critical human-facing domains, ensuring the integrity and trustworthiness of ML models becomes all-important.
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