Problem
Research questions and friction points this paper is trying to address.
Federated Learning as a Service
Sustainability Constraints
Carbon Feasibility
Heterogeneous Constraints
Designing Sustainable Federated Learning as a Service using Neural Architecture Search
🤖 AI Summary
This study addresses the challenges of infeasible user participation and training instability caused by carbon constraints in Federated Learning as a Service (FLaaS). We propose SFLaaS, a novel framework that innovatively constructs a demand-driven search space and a carbon feasibility assessment mechanism. Integrated with an adaptive scheduling strategy, this approach jointly optimizes model performance and user participation under hard carbon constraints. Experimental results demonstrate that SFLaaS effectively ensures federated training stability under heterogeneous sustainability constraints. Furthermore, it achieves simultaneous improvements in carbon compliance and model accuracy, establishing a new paradigm for green federated learning.
📝 Abstract
The sustainability constraints of FLaaS consumers pose significant challenges to maintaining carbon-feasible federated training in FLaaS environments. These constraints often lead to infeasible consumer participation and unstable federated training under hard carbon constraints. We propose a Sustainable Federated Learning as a Service (SFLaaS), a carbon- constrained Neural Architecture Search (NAS) framework for heteroge- neous sustainable constraints. We introduce a requirement-driven search space that transforms consumer sustainability profiles into a feasible architecture region before federated execution. We develop a consumer-level carbon feasibility estimation mechanism to evaluate candidate architectures under dynamic carbon conditions. We propose a sustainable con- sumer scheduling strategy that adaptively selects feasible consumers and allocates local workloads to preserve consumer participation and statistical data coverage. An evolutionary search strategy jointly optimised for predictive performance, consumer feasibility, and participation coverage under hard carbon constraints. Experiments on real-world datasets and a simulated environment demonstrate the effectiveness of the proposed approach.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Sustainable Federated Learning
Neural Architecture Search
Carbon-constrained NAS
Requirement-driven Search Space
Adaptive Consumer Scheduling
🔎 Similar Papers
No similar papers found.
