Position: Federated Learning is a Lens towards a Democratized Future for the Scaling Law Era

Machine learning (ML) systems have grown significantly in size and popularity over recent years. However, the data and computation power supply chains which have helped fuel this growth have not been built without controversy. In particular, some of the data used to train these models may have been used without permission, while the growing appetite for compute power in model training increasingly incentivizes consolidation of access to larger players. As some stakeholders, such as data owners and everyday consumers of the Internet, have felt left behind by the emerging ML ecosystem, we seek to use federated learning paradigm as a model and motivation to develop a more democratized future for the ML community: one that is more decentralized, cooperative, and accountable. This position paper argues that the original proposition of federated learning as a framework enabling cooperation, privacy, and decentralization is still relevant today, even after the emergence of large foundation model- and scaling law-driven ML research, and that FL can inspire alternative ML ecosystems which alleviate and avoid the current frictions of large ML systems.