CNS Core: Small: FLINT: Robust Federated Learning for Internet of Things

FLINT: Robust Federated Learning for Internet of Things Abstract: Federated learning enables machine learning on distributed datasets without needing the learner to access directly the datasets owned by respective stakeholders. The Internet of Things (IoT) provides a fertile ground for applying federated learning, where distributed IoT devices produce a plethora of data that are often private. However, IoT devices are vulnerable to environments with inaccurate data samples and malicious attacks, which is a signi?cant challenge for federated learning. Agglomerating data in a federated and robust manner may produce bene?ts to the economy and society. Objectives of the Robust Federated Learning for Internet of Things (FLINT) project include: (1) Formulate federated learning (FL) in heterogeneous, dynamic IoT environments with unreliable and adversarial clients. (2) Design new FL algorithms that are robust against hostile conditions with benign, unreliable, and malicious clients injecting erroneous or poisonous data. (3) Design novel incentive mechanisms to ensure rational clients gain non- negative utility by contributing training data and resources. (4) Analyze complexity, performance, and theoretical bounds of proposed algorithms. (5) Build an IoT testbed to study the learning performance of robust FL solutions. (6) Simulation experiments on real- world datasets to evaluate performance scalability. The FLINT project will offer graduate and undergrad students a unique opportunity to gain interdisciplinary education in the design of robust FL algorithms for IoT. Research ?ndings will enrich courses on cyber-physical systems and machine learning for IoT.