St. Cloud Technical and Community College

Federated learning (FL) provides a decentralized approach to artificial intelligence (AI) model training, enabling multiple devices or systems to collaboratively train models without sharing raw data. The core challenge lies in maintaining data privacy, security, and the overall efficiency of model convergence across distributed networks. This paper proposes a novel framework for secure federated learning that utilizes advanced encryption techniques, secure aggregation protocols, and differential privacy mechanisms to enhance both privacy and model accuracy. Experimental results from a real-world use case demonstrate the efficiency of this framework, with the proposed model outperforming existing solutions in terms of model convergence speed and privacy protection. The findings suggest that federated learning is a promising paradigm for AI model training in industries that require high data security, such as healthcare and finance.

Abstract Richard Hare argues that the fundamental assumptions of Kant's ethical system should have led Kant to utilitarianism, had Kant not confused a norm's generality with its universality, and hence adopted rigorist, deontological norms. Several authors, including Jens Timmermann, have argued contra Hare that the gap between Kantian and utilitarian/consequentialist ethics is fundamental and cannot be bridged. This article shows that Timmermann's claims rely on a systematic failure to separate normative and metaethical aspects of each view, and that Hare's attempt to bridge the gap between Kantian and consequentialist ethics is immune to Timmermann's criticisms. Furthermore, the term “Kantian ethics” is often misleading, and should typically be qualified as either “Kantian rationalism” or “Kantian deontology” in order to avoid confusions of the sort Timmermann falls into.