We develop a framework with a dynamic server selection and study convergence and security conditions. Quantum Federated Learning (QFL) is an emerging area with several studies in recent years; see (Larasati et al.) and references therein. In a different context, secure blockchain-based FL is proposed in (Xu et al., 2023). However, post-quantum secure QFL is poorly studied in the literature. We develop a preliminary study with a proof of concept model of post-quantum secure QFL.

On May 17, 2021, CAIDP awarded certificates to several individuals who "successfully completed a comprehensive program, including research, writing, and policy analysis, in ARTIFICIAL INTELLIGENCE POLICY." The CAIDP AI Policy Certification requires completion of a detailed multiple-choice test in Ai History, AI Issues and Institutions, AI Regulation, and Research Methods. Candidates are also required to complete a Statement of Professional Ethics for AI Policy and a policy analysis assignment. The recipients of the CAIDP 2021 AI Policy Certificate will be known as the Giovanni Buttarelli Inaugural Class, in memory of the former European Data Protection Supervisor. In Il futuro della privacy e la vivacità della democrazia in Privacy 2030: Una nuova visione per l'Europa (in English), Giovanni warned that a digital underclass has emerged.

The use of artificial intelligence and machine learning is almost commonplace in the biotech and pharma industry as multiple companies are harnessing the power to aid in drug discovery and development. This week more companies have announced advancements in their AI programming. This morning, San Francisco-based Notable Labs announced it secured $40 million in a Series B funding round to use its artificial intelligence platform to advance cancer drug development. The company's approach is aimed at predicting which types of patients are most likely to respond to a drug in as little as five days. The process is designed to help physicians make more informed decisions about which clinical trials will be effective with patients and can also benefit the likelihood of a trial's success by matching the right patients to the right trial.