# Designing Sustainable Federated Learning as a Service using Neural Architecture Search

arXiv: https://arxiv.org/abs/2608.14359 · HTML: https://mlsanity.com/paper/2608.14359/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://mlsanity.com/paper/1812.00332/) (2018)
- [FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://mlsanity.com/paper/1812.03443/) (2018)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [EcoNAS: Finding Proxies for Economical Neural Architecture Search](https://mlsanity.com/paper/2001.01233/) (2020)
- [KNAS: Green Neural Architecture Search](https://mlsanity.com/paper/2111.13293/) (2021)
- [FLaaS: Federated Learning as a Service](https://mlsanity.com/paper/2011.09359/) (2020)
- [FedGreen: Federated Learning with Fine-Grained Gradient Compression for Green Mobile Edge Computing](https://mlsanity.com/paper/2111.06146/) (2021)
- [Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling](https://mlsanity.com/paper/2509.08980/) (2025)
- [NebulaFL: Effective Asynchronous Federated Learning for JointCloud Computing](https://mlsanity.com/paper/2412.04868/) (2024)

## Abstract (arXiv)

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.