A Typed Tensor Language for Federated Learning
Researchers have developed a new typed tensor language to formalize the structure of federated learning and analytics. This language distinguishes between federated tensors partitioned across clients and shared tensors available globally. A key finding is a shared-state factorization theory, demonstrating that one-round federated programs can be factored through fixed-dimensional shared state independent of client count. AI
IMPACT Formalizes federated learning computations, potentially enabling more efficient and scalable distributed AI model training.