GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning

A critical change is happening in today's Internet of Things (IoT). The computational power of edge devices is steadily increasing. AI chips are rapidly infiltrating the market, smart phones nowadays have compute power comparable to everyday use laptops (Samsung 2019), Tesla just boasted that its autopilot system has computing power of more than 3000 MacBook pros (CleanTechnica 2021) and small local computers such as Raspberry Pis have become common place in many applications especially manufacturing (Al-Ali et al. 2018). This opens a new paradigm for data analytics in IoT; one that exploits local computing power to process more of the user's data where it is created. This future of IoT has been recently termed as the "The Internet of Federated Things (IoFT)" (Kontar et al. 2021) where the term federated, refers to some autonomy for IoT devices and is inspired by the explosive recent interest in federated data science.