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Found 4 papers, 2 papers with code
FLEX: FLEXible Federated Learning Framework
In the realm of Artificial Intelligence (AI), the need for privacy and security in data processing has become paramount.
Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges
Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence.
Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning
We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model.
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack of the needed unified vision for those techniques, and a methodological workflow that support their use.
