X @Avi Chawla

Federated Learning Overview - Federated learning addresses the challenge of training ML models on private data residing on user devices [3] - It dispatches a model to end devices for training on private data and aggregates the trained models on a central server [4] - This approach reduces computation requirements on the server side by distributing most computation to user devices [3] Challenges in Federated Learning - Client devices have limited RAM and battery power, requiring efficient training methods [3] - Aggregating different models received from client devices to create a central model poses a challenge [3] - Privacy-sensitive datasets are often biased with personal likings and beliefs, leading to skewness in client data distribution [5] Data Privacy and Bias - Data on modern devices, such as images, messages, and voice notes, is mostly private [2][4] - The skewness in client data distribution, such as an overrepresentation of pet, car, or travel images, needs to be addressed [5] Application in Speech Recognition - Siri processes 25 billion (25B) requests per month, representing a large dataset for improving speech recognition [1] - The data on user devices can be leveraged to improve ML models [1]