# Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity

arXiv: https://arxiv.org/abs/2005.12326 · HTML: https://mlsanity.com/paper/2005.12326/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 May 2020.

## Built on by (most cited first, 20 of 20)

- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning](https://mlsanity.com/paper/2107.08147/) (2021)
- [FedAdapt: Adaptive Offloading for IoT Devices in Federated Learning](https://mlsanity.com/paper/2107.04271/) (2021)
- [Prototype Guided Federated Learning of Visual Feature Representations](https://mlsanity.com/paper/2105.08982/) (2021)
- [Towards Energy-Aware Federated Learning on Battery-Powered Clients](https://mlsanity.com/paper/2208.04505/) (2022)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data](https://mlsanity.com/paper/2203.01214/) (2022)
- [Promoting Data and Model Privacy in Federated Learning through Quantized LoRA](https://mlsanity.com/paper/2406.10976/) (2024)
- [Joint Optimization of Energy Consumption and Completion Time in Federated Learning](https://mlsanity.com/paper/2209.14900/) (2022)
- [Accelerating Federated Learning with a Global Biased Optimiser](https://mlsanity.com/paper/2108.09134/) (2021)
- [Faster Federated Learning with Decaying Number of Local SGD Steps](https://mlsanity.com/paper/2305.09628/) (2023)
- [Resource-Efficient Federated Fine-Tuning Large Language Models for Heterogeneous Data](https://mlsanity.com/paper/2503.21213/) (2025)
- [Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running](https://mlsanity.com/paper/2204.13878/) (2022)
- [FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning](https://mlsanity.com/paper/2211.16669/) (2022)
- [Take Your Pick: Enabling Effective Personalized Federated Learning within Low-dimensional Feature Space](https://mlsanity.com/paper/2307.13995/) (2023)
- [Scheduling Algorithms for Federated Learning with Minimal Energy Consumption](https://mlsanity.com/paper/2209.06210/) (2022)
- [Dual-Segment Clustering Strategy for Hierarchical Federated Learning in Heterogeneous Wireless Environments](https://mlsanity.com/paper/2405.09276/) (2024)
- [FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning](https://mlsanity.com/paper/2411.11713/) (2024)
- [Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Information](https://mlsanity.com/paper/2411.07841/) (2024)
- [Client and Training Data Selection for Computationally Efficient Synchronized Federated Learning](https://mlsanity.com/paper/2609.39250/) (2026)

## Abstract (arXiv)

Originated from distributed learning, federated learning enables privacy-preserved collaboration on a new abstracted level by sharing the model parameters only. While the current research mainly focuses on optimizing learning algorithms and minimizing communication overhead left by distributed learning, there is still a considerable gap when it comes to the real implementation on mobile devices. In this paper, we start with an empirical experiment to demonstrate computation heterogeneity is a more pronounced bottleneck than communication on the current generation of battery-powered mobile devices, and the existing methods are haunted by mobile stragglers. Further, non-identically distributed data across the mobile users makes the selection of participants critical to the accuracy and convergence. To tackle the computational and statistical heterogeneity, we utilize data as a tuning knob and propose two efficient polynomial-time algorithms to schedule different workloads on various mobile devices, when data is identically or non-identically distributed. For identically distributed data, we combine partitioning and linear bottleneck assignment to achieve near-optimal training time without accuracy loss. For non-identically distributed data, we convert it into an average cost minimization problem and propose a greedy algorithm to find a reasonable balance between computation time and accuracy. We also establish an offline profiler to quantify the runtime behavior of different devices, which serves as the input to the scheduling algorithms. We conduct extensive experiments on a mobile testbed with two datasets and up to 20 devices. Compared with the common benchmarks, the proposed algorithms achieve 2-100x speedup epoch-wise, 2-7% accuracy gain and boost the convergence rate by more than 100% on CIFAR10.