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Found 6 papers, 2 papers with code
Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study
This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task.
Toward efficient resource utilization at edge nodes in federated learning
In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round.
FedCSD: A Federated Learning Based Approach for Code-Smell Detection
This was followed by experiment 2, which was concerned with cross-evaluation, where each ML model was trained using one dataset, which was then evaluated over the other two datasets.
FedBot: Enhancing Privacy in Chatbots with Federated Learning
The POC combines Deep Bidirectional Transformer models and federated learning algorithms to protect customer data privacy during collaborative model training.
FedQAS: Privacy-aware machine reading comprehension with federated learning
In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQUAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
Scalable federated machine learning with FEDn
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.
