# Cost-Effective Federated Learning Design

arXiv: https://arxiv.org/abs/2012.08336 · HTML: https://mlsanity.com/paper/2012.08336/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 30 of 62)

- [FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout](https://mlsanity.com/paper/2102.13451/) (2021)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Cost-Effective Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/2109.05411/) (2021)
- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reduction](https://mlsanity.com/paper/2310.08670/) (2023)
- [FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extraction](https://mlsanity.com/paper/2407.19389/) (2024)
- [Ten Challenging Problems in Federated Foundation Models](https://mlsanity.com/paper/2502.12176/) (2025)
- [AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systems](https://mlsanity.com/paper/2311.05842/) (2023)
- [Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Design](https://mlsanity.com/paper/2207.09387/) (2022)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset](https://mlsanity.com/paper/2310.14906/) (2023)
- [Efficient Federated Meta-Learning over Multi-Access Wireless Networks](https://mlsanity.com/paper/2108.06453/) (2021)
- [Federated Learning with Flexible Control](https://mlsanity.com/paper/2212.08496/) (2022)
- [Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings](https://mlsanity.com/paper/2102.02849/) (2021)
- [Federated Few-Shot Learning with Adversarial Learning](https://mlsanity.com/paper/2104.00365/) (2021)
- [Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysis](https://mlsanity.com/paper/2110.07567/) (2021)
- [On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks](https://mlsanity.com/paper/2111.07911/) (2021)
- [Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification](https://mlsanity.com/paper/2311.06141/) (2023)
- [Service Delay Minimization for Federated Learning over Mobile Devices](https://mlsanity.com/paper/2205.09868/) (2022)
- [Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2404.13804/) (2024)
- [Time-sensitive Learning for Heterogeneous Federated Edge Intelligence](https://mlsanity.com/paper/2301.10977/) (2023)
- [Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participation](https://mlsanity.com/paper/2304.07981/) (2023)
- [Federated Momentum Contrastive Clustering](https://mlsanity.com/paper/2206.05093/) (2022)
- [Addressing Heterogeneity in Federated Learning via Distributional Transformation](https://mlsanity.com/paper/2210.15025/) (2022)
- [Multi-Source to Multi-Target Decentralized Federated Domain Adaptation](https://mlsanity.com/paper/2304.12422/) (2023)
- [Towards Communication-Learning Trade-off for Federated Learning at the Network Edge](https://mlsanity.com/paper/2205.14271/) (2022)
- [A Fast Blockchain-based Federated Learning Framework with Compressed Communications](https://mlsanity.com/paper/2208.06095/) (2022)
- [Joint Optimization of Energy Consumption and Completion Time in Federated Learning](https://mlsanity.com/paper/2209.14900/) (2022)
- [ModularFed: Leveraging Modularity in Federated Learning Frameworks](https://mlsanity.com/paper/2212.10427/) (2022)

## Abstract (arXiv)

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different metric preferences. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for various datasets, different machine learning models, and heterogeneous system settings.