Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
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. We propose GradsSharding, which instead partitions the gradient tensor into M shards, each averaged independently by a serverless function that receives contributions from all clients. Because FedAvg averaging is element-wise, this produces bit-identical results to tree-based approaches, so mode...
| Authors | Amine Barrak |
|---|---|
| Primary category | cs.DC |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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