# A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison

arXiv: https://arxiv.org/abs/2110.10223 · HTML: https://mlsanity.com/paper/2110.10223/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 38 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Federated Learning with Personalization Layers](https://mlsanity.com/paper/1912.00818/) (2019)
- [FedMD: Heterogenous Federated Learning via Model Distillation](https://mlsanity.com/paper/1910.03581/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Bayesian Nonparametric Federated Learning of Neural Networks](https://mlsanity.com/paper/1905.12022/) (2019)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Framework](https://mlsanity.com/paper/2002.10671/) (2020)
- [Arcades: A deep model for adaptive decision making in voice controlled smart-home](https://mlsanity.com/paper/1807.01970/) (2018)

## Built on by (most cited first, 30 of 31)

- [FedMultimodal: A Benchmark For Multimodal Federated Learning](https://mlsanity.com/paper/2306.09486/) (2023)
- [FedStack: Personalized activity monitoring using stacked federated learning](https://mlsanity.com/paper/2209.13080/) (2022)
- [Threats and Defenses in Federated Learning Life Cycle: A Comprehensive Survey and Challenges](https://mlsanity.com/paper/2407.06754/) (2024)
- [FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning](https://mlsanity.com/paper/2309.10283/) (2023)
- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Transformer-based Models to Deal with Heterogeneous Environments in Human Activity Recognition](https://mlsanity.com/paper/2209.11750/) (2022)
- [Federated Continual Learning through distillation in pervasive computing](https://mlsanity.com/paper/2207.08181/) (2022)
- [Client-specific Property Inference against Secure Aggregation in Federated Learning](https://mlsanity.com/paper/2303.03908/) (2023)
- [A Survey of Federated Evaluation in Federated Learning](https://mlsanity.com/paper/2305.08070/) (2023)
- [Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification](https://mlsanity.com/paper/2410.15681/) (2024)
- [Evaluation and comparison of federated learning algorithms for Human Activity Recognition on smartphones](https://mlsanity.com/paper/2210.16918/) (2022)
- [LLM-Prior: A Framework for Knowledge-Driven Prior Elicitation and Aggregation](https://mlsanity.com/paper/2508.03766/) (2025)
- [Federated learning framework for collaborative remaining useful life prognostics: an aircraft engine case study](https://mlsanity.com/paper/2506.00499/) (2025)
- [Federated Learning Aggregation: New Robust Algorithms with Guarantees](https://mlsanity.com/paper/2205.10864/) (2022)
- [Zone-based Federated Learning for Mobile Sensing Data](https://mlsanity.com/paper/2303.06246/) (2023)
- [Scalable Collaborative Learning via Representation Sharing](https://mlsanity.com/paper/2211.10943/) (2022)
- [A review of federated learning in renewable energy applications: Potential, challenges, and future directions](https://mlsanity.com/paper/2312.11220/) (2023)
- [Federated Learning and catastrophic forgetting in pervasive computing: demonstration in HAR domain](https://mlsanity.com/paper/2207.08180/) (2022)
- [Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients](https://mlsanity.com/paper/2305.09856/) (2023)
- [FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning](https://mlsanity.com/paper/2311.06917/) (2023)
- [Federated Self-Supervised Learning in Heterogeneous Settings: Limits of a Baseline Approach on HAR](https://mlsanity.com/paper/2207.08187/) (2022)
- [Federated Learning on Edge Sensing Devices: A Review](https://mlsanity.com/paper/2311.01201/) (2023)
- [FedControl: When Control Theory Meets Federated Learning](https://mlsanity.com/paper/2205.14236/) (2022)
- [Tackling Computational Heterogeneity in FL: A Few Theoretical Insights](https://mlsanity.com/paper/2307.06283/) (2023)
- [SoK: Assessing the State of Applied Federated Machine Learning](https://mlsanity.com/paper/2308.02454/) (2023)
- [Evaluating Multi-Global Server Architecture for Federated Learning](https://mlsanity.com/paper/2311.15382/) (2023)
- [Federated Computing -- Survey on Building Blocks, Extensions and Systems](https://mlsanity.com/paper/2404.02779/) (2024)
- [CDFL: Efficient Federated Human Activity Recognition using Contrastive Learning and Deep Clustering](https://mlsanity.com/paper/2407.12287/) (2024)
- [FedAvgen: Metadata for Model Aggregation In Communication Systems](https://mlsanity.com/paper/2505.05486/) (2025)
- [Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response](https://mlsanity.com/paper/2509.13987/) (2025)

## Abstract (arXiv)

Pervasive computing promotes the installation of connected devices in our living spaces in order to provide services. Two major developments have gained significant momentum recently: an advanced use of edge resources and the integration of machine learning techniques for engineering applications. This evolution raises major challenges, in particular related to the appropriate distribution of computing elements along an edge-to-cloud continuum. About this, Federated Learning has been recently proposed for distributed model training in the edge. The principle of this approach is to aggregate models learned on distributed clients in order to obtain a new, more general model. The resulting model is then redistributed to clients for further training. To date, the most popular federated learning algorithm uses coordinate-wise averaging of the model parameters for aggregation. However, it has been shown that this method is not adapted in heterogeneous environments where data is not identically and independently distributed (non-iid). This corresponds directly to some pervasive computing scenarios where heterogeneity of devices and users challenges machine learning with the double objective of generalization and personalization. In this paper, we propose a novel aggregation algorithm, termed FedDist, which is able to modify its model architecture (here, deep neural network) by identifying dissimilarities between specific neurons amongst the clients. This permits to account for clients' specificity without impairing generalization. Furthermore, we define a complete method to evaluate federated learning in a realistic way taking generalization and personalization into account. Using this method, FedDist is extensively tested and compared with three state-of-the-art federated learning algorithms on the pervasive domain of Human Activity Recognition with smartphones.