# Resource-Efficient Federated Learning

arXiv: https://arxiv.org/abs/2111.01108 · HTML: https://mlsanity.com/paper/2111.01108/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 91 references that are in the map.
- First posted on arXiv 1 Nov 2021.

## Builds on (most cited first, 30 of 46)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Improving Federated Learning Personalization via Model Agnostic Meta Learning](https://mlsanity.com/paper/1909.12488/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [Revisiting Distributed Synchronous SGD](https://mlsanity.com/paper/1702.05800/) (2017)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [Federated Visual Classification with Real-World Data Distribution](https://mlsanity.com/paper/2003.08082/) (2020)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation](https://mlsanity.com/paper/1903.07424/) (2019)

## Built on by (most cited first, 26 of 26)

- [FedZero: Leveraging Renewable Excess Energy in Federated Learning](https://mlsanity.com/paper/2305.15092/) (2023)
- [Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study](https://mlsanity.com/paper/2403.17287/) (2024)
- [Green Federated Learning](https://mlsanity.com/paper/2303.14604/) (2023)
- [Federated Momentum Contrastive Clustering](https://mlsanity.com/paper/2206.05093/) (2022)
- [Boosting Asynchronous Decentralized Learning with Model Fragmentation](https://mlsanity.com/paper/2410.12918/) (2024)
- [Flashback: Understanding and Mitigating Forgetting in Federated Learning](https://mlsanity.com/paper/2402.05558/) (2024)
- [Federated Learning in IoT: a Survey from a Resource-Constrained Perspective](https://mlsanity.com/paper/2308.13157/) (2023)
- [LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning](https://mlsanity.com/paper/2405.10968/) (2024)
- [Learnable Sparse Customization in Heterogeneous Edge Computing](https://mlsanity.com/paper/2412.07216/) (2024)
- [Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling](https://mlsanity.com/paper/2509.08980/) (2025)
- [Leveraging The Edge-to-Cloud Continuum for Scalable Machine Learning on Decentralized Data](https://mlsanity.com/paper/2306.10848/) (2023)
- [EcoLearn: Optimizing the Carbon Footprint of Federated Learning](https://mlsanity.com/paper/2310.17972/) (2023)
- [An Empirical Study of Efficiency and Privacy of Federated Learning Algorithms](https://mlsanity.com/paper/2312.15375/) (2023)
- [Context-Aware Orchestration of Energy-Efficient Gossip Learning Schemes](https://mlsanity.com/paper/2404.12023/) (2024)
- [Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning](https://mlsanity.com/paper/2312.10425/) (2023)
- [Harnessing Increased Client Participation with Cohort-Parallel Federated Learning](https://mlsanity.com/paper/2405.15644/) (2024)
- [Where is the Testbed for my Federated Learning Research?](https://mlsanity.com/paper/2407.14154/) (2024)
- [FLStore: Efficient Federated Learning Storage for non-training workloads](https://mlsanity.com/paper/2503.00323/) (2025)
- [Accelerating MoE Model Inference with Expert Sharding](https://mlsanity.com/paper/2503.08467/) (2025)
- [Benchmarking Mutual Information-based Loss Functions in Federated Learning](https://mlsanity.com/paper/2504.11877/) (2025)
- [FedOptima: Optimizing Resource Utilization in Federated Learning](https://mlsanity.com/paper/2504.13850/) (2025)
- [RIFLES: Resource-effIcient Federated LEarning via Scheduling](https://mlsanity.com/paper/2505.13169/) (2025)
- [FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments](https://mlsanity.com/paper/2505.13576/) (2025)
- [Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids](https://mlsanity.com/paper/2508.08022/) (2025)
- [FlashbackCL: Mitigating Temporal Forgetting in Federated Learning](https://mlsanity.com/paper/2606.03939/) (2026)
- [HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning](https://mlsanity.com/paper/2606.07621/) (2026)

## Abstract (arXiv)

Federated Learning (FL) enables distributed training by learners using local data, thereby enhancing privacy and reducing communication. However, it presents numerous challenges relating to the heterogeneity of the data distribution, device capabilities, and participant availability as deployments scale, which can impact both model convergence and bias. Existing FL schemes use random participant selection to improve fairness; however, this can result in inefficient use of resources and lower quality training. In this work, we systematically address the question of resource efficiency in FL, showing the benefits of intelligent participant selection, and incorporation of updates from straggling participants. We demonstrate how these factors enable resource efficiency while also improving trained model quality.