Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning

ArXi:2604.22072v1 Announce Type: cross Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clients across aggregators, but every aggregator must hold the complete model gradient in memory. When gradients exceed the per-function memory limit (e.g., 10 GB on AWS Lambda), aggregation becomes infeasible regardless of tree depth or branching factor.