Arno Geimer
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View article: WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning
WallStreetFeds: Client-Specific Tokens as Investment Vehicles in Federated Learning Open
Federated Learning (FL) is a collaborative machine learning paradigm which allows participants to collectively train a model while training data remains private. This paradigm is especially beneficial for sectors like finance, where data p…
View article: On the Volatility of Shapley-Based Contribution Metrics in Federated Learning
On the Volatility of Shapley-Based Contribution Metrics in Federated Learning Open
Federated learning (FL) is a collaborative and privacy-preserving Machine Learning paradigm, allowing the development of robust models without the need to centralize sensitive data. A critical challenge in FL lies in fairly and accurately …
View article: NLAC: A Self-Maintained Trust Overlay for the XRP Ledger
NLAC: A Self-Maintained Trust Overlay for the XRP Ledger Open
This paper presents NLAC, a framework for creating, managing, and maintaining trusted Unique Node Lists (UNLs) for the XRP Ledger. NLAC consists of three modules that automate the generation of UNLs, including membership management, classi…