DRL-Based Cost-Driven Adaptive Logic Tree Generation for Serverless Federated Learning Aggregation

The rise of serverless computing has prompted service providers to offer fine-grained function configurations, such as allocating memory in 1MB increments, to support a wide range of function types and adapt to fluctuating workloads. Implementing a Federated Learning (FL) aggregator using serverless functions significantly minimizes the cost associated with idle waiting time. However, for serverless Federated Learning as a Service (FLaaS) providers, determining cost-effective function configurations is challenging. This is primarily because predicting future workload patterns, such as the number of parties involved, is difficult and often inaccurate. In this paper, we use Alibaba Cloud Function Compute as a case study to analyze serverless pricing and address key challenges in optimizing function execution costs within the context of logic-tree-based FL aggregation. We then propose an offline optimal algorithm based on dynamic programming, assuming the future workload pattern is known, as a benchmark. To accommodate online workload arrivals, specifically the variable number of parties in each round, we propose a deep Reinforcement Learning based adaptive logic Tree Generation method (RLTreeGen), which dynamically constructs cost-efficient aggregation architectures for each round of FL training. We evaluate its cost performance using real-world traces and Alibaba Cloud Function Compute’s pricing model. The experimental results demonstrate that RLTreeGen outperforms other online benchmark algorithms and closely approaches the offline optimal in all tested scenarios.