MOCHA: Federated Multi-Task Learning[Paper][NIPS 2017][Slides] Variational Federated Multi-Task Learning[Paper] Federated Kernelized Multi-Task Learning[Paper] Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints[Paper][NIPS 2019 Workshop] ...
7.2 Multi-task Learning 7.3 Hierarchical FL Part 11: Federated with Deep learning 11.1 Neural Architecture Search(NAS) 13.2 Secret Sharing Part 13: Secure Multi-party Computation(MPC) 13.1 Differential Privacy 14.2 Natual Language Processing
Existing works on federated contextual bandits rely on linear or kernelized bandits, which may fall short when modeling complex real-world reward functions. So, this paper introduces the federated neural-upper confidence bound (FN-UCB) algorithm. To better exploit the federated setting, FN-UCB ...
The amount of data generated owing to the rapid development of the Smart Internet of Things is increasing exponentially. Traditional machine learning can n
We build a visual leader-following framework [10], using the Single Shot MultiBox Detector (SSD), Kernelized Correlation Filters (KCF) and Person Reidentification (Re-ID), which can stably detect person. Concurrently, a LiDAR-based leader-following framework is built, which can effectively ...
Lastly, such vehicles can be tracked using Kalman filter (KF), kernelized filter-based approaches for coping with and accomplishing huge traffic flows with lesser human intervention. Several models exist in the literature to perform the classification process. Though several ML and DL models for ...