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UNDERSTANDING FEDERATED LEARNING AND ITS APPLICATION IN VIDEO ANOMALY DETECTION

—Federated Learning is a relatively newer approach, and it tries to solve problems such as reluctance of clients to share data. Instead, a model is sent from server to client(s) where training happens on various edge clients who later update the central model. This is a redesign of the traditional approach of learning from data where all data had to be uploaded to a central server. In contrast, federated learning promotes the idea of bringing code to data which can help overcome latency in timecritical operations. Privacy gives every individual a right to prevent release of data, and several machine learning use-cases deal with such sensitive information for e.g., disease prediction (patient data), anomaly detection (security footage) etc. By introducing the use of federated learning, we can overcome the issue of data sharing and all data will be retained at the edge nodes where the training happens. Through this work we walk through a system design of applying federated learning in a real-time use case of video anomaly detection and present our preliminary results where we achieved an accuracy of 83.34% using the federated paradigm, higher than the centralized accuracy of 81.29%.