In federated learning, embeddings play a vital role by enabling local models to learn useful representations of data without the need to share raw data across devices. Federated learning allows multiple devices or edge nodes to collaboratively train a model while keeping the data decentralized, ensuring privacy and security. Embeddings help these local models generate compact and meaningful representations of data that can be used for training without transmitting sensitive information.
For example, in federated learning for text-based applications, devices may learn word or document embeddings locally on the device and then share updates to the model (such as weight updates or gradient information) rather than the actual embeddings themselves. This ensures that privacy is maintained, as sensitive data is never shared with the central server.
Embeddings are particularly useful in federated learning for natural language processing (NLP) tasks, image recognition, and recommendation systems, as they provide rich and compact representations of data that can be easily updated and aggregated from multiple local devices. Over time, these federated models can improve by learning from diverse datasets across devices, resulting in better generalization to new, unseen data while maintaining privacy.