Spry is a federated learning algorithm that enables finetuning LLMs using Forward-mode Auto Differentiation; to achieve low memory footprint, high accuracy, and fast convergence.

Flow addresses the challenge of statistical heterogeneity in federated learning through creating a dynamic personalized model for each input instance through a routing mechanism.

Flash uses client-side early-stopping training to facilitate detection of concept drifts and the server-side drift-aware adaptive optimization in a Federated Learning setup, to solve concept drift detection and adaptation challenges.

Multi-agent chatbots, like CommunityBots, improve user engagement and response quality over single-agent systems in multi-topic conversations by effectively managing topic and agent transitions.