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 model accuracy is invariant by construction. Per-function memory is bounded at O(|θ|/M), independent of client count, enabling aggregation of arbitrarily large models. We evaluate GradsSharding against lambda-FL and LIFL through HPC experiments and real AWS Lambda deployments across model sizes from 43 MB to 5 GB. Results show a cost crossover at approximately 500 MB gradient size, 2.7x cost reduction at VGG-16 scale, and that GradsSharding is the only architecture that remains deployable beyond the serverless memory ceiling.

翻译：联邦学习（FL）在无服务器平台上的聚合面临严峻的可扩展性瓶颈：现有架构（lambda-FL、LIFL）通过将客户端划分至不同聚合器，但每个聚合器必须在内存中保存完整的模型梯度。当梯度超过单函数内存限制（如AWS Lambda的10 GB）时，无论树深度或分支因子如何调整，聚合均不可行。我们提出GradsSharding方法，将梯度张量划分为M个分片，每个分片由一个接收所有客户端贡献的无服务器函数独立求平均。由于FedAvg聚合为逐元素操作，该方法与基于树的聚合方式在比特级上结果一致，因此模型精度在构造上保持不变。单函数内存上限为O(|θ|/M)，与客户端数量无关，从而实现对任意大规模模型的聚合。我们通过HPC实验与真实AWS Lambda部署，在模型大小从43 MB到5 GB的范围内，将GradsSharding与lambda-FL、LIFL进行对比评估。结果表明，在梯度大小约为500 MB时出现成本转折点；在VGG-16规模下成本降低2.7倍；且GradsSharding是唯一在无服务器内存极限以上仍可部署的架构。