Conventional federated learning (FL) frameworks follow a server-driven model where the server determines session initiation and client participation, which faces challenges in accommodating clients' asynchronous needs for model updates. We introduce Client-Driven Federated Learning (CDFL), a novel FL framework that puts clients at the driving role. In CDFL, each client independently and asynchronously updates its model by uploading the locally trained model to the server and receiving a customized model tailored to its local task. The server maintains a repository of cluster models, iteratively refining them using received client models. Our framework accommodates complex dynamics in clients' data distributions, characterized by time-varying mixtures of cluster distributions, enabling rapid adaptation to new tasks with superior performance. In contrast to traditional clustered FL protocols that send multiple cluster models to a client to perform distribution estimation, we propose a paradigm that offloads the estimation task to the server and only sends a single model to a client, and novel strategies to improve estimation accuracy. We provide a theoretical analysis of CDFL's convergence. Extensive experiments across various datasets and system settings highlight CDFL's substantial advantages in model performance and computation efficiency over baselines.

The growing popularity of artificial intelligence (AI) programmes, which have shown themselves increasingly capable in recent months of generating images, videos, music, computer programming code and even texts of all kinds in a matter of seconds, producing seemingly appropriate and coherent results, in many instances – and in many others, not – is arousing fascination and concern all over the world, especially among artists and creators. What the AI tools of today can do is, at times, so spectacular and convincing that it is hard not to think it must be the work of a conscious being that comprehends what is being asked of it and understands what it produces in response. This is clearly not the case, but for the public at large it suddenly seems like we are witnessing the sudden emergence of revolutionary technology, full of potential and promise but also perils that could transform our world. This day may come, but it is further away than the flurry of expectation may lead us to think. What has happened in recent months, above all, is that the current technology, quite widespread and known to all researchers who had hitherto been experimenting with it behind closed doors, has suddenly started to see the light of day, not only with a view to introducing it to the public, arousing interest and attracting investors, but also so that the programmes could benefit from interacting with people and be'trained' by millions of requests and users at the same time, a massive amount of activity and information that no company could otherwise secure for their AIs.