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# A Survey of Federated Learning Models for Human Activity Recognition

Claudio Palma, Tarun Venkatesh, Christophe Lohr

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## To cite this version:

Claudio Palma, Tarun Venkatesh, Christophe Lohr. A Survey of Federated Learning Models for Human Activity Recognition. 2025. hal-05273360

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Preprint submitted on 22 Sep 2025

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A Survey of Federated Learning Models for Human
Activity Recognition

Claudio Palma
IMT Atlantique

Rennes, France

Email: claudio.palma@imt-atlantique.net

Tarun Venkatesh
IMT Atlantique

Rennes, France

Email: tarun.venkatesh@imt-atlantique.net

**Abstract** — Human Activity Recognition involves the use of sensors equipped in devices to gather data on daily activities. Federated Learning is an approach that decentralizes model training for the collected data, but has limitations that can impact the processing and privacy of data. From previous research on various Federated Learning frameworks, FedHAR, ProtoHAR, and FedAvg are the most promising frameworks, they propose to make predictions with data processing and tackle these limitations using different techniques. Every framework conducted experiments on several datasets to show the benefits of those frameworks in tackling these limitations. This paper proposes a comparison between the three frameworks to indicate which can ensure a good performance in all metrics and the privacy of the data. Finally, future works are described to propose new directions for upcoming research.

**Keywords**—Internet of Things, Human Activity Recognition, Federated Learning, accuracy, efficiency, security, privacy

# I. INTRODUCTION

Human activity recognition (HAR) is a technique that uses sensor-equipped smart devices to track and categorise daily activities based on data gathered. HAR is widely used in areas like smart homes, health monitoring, fitness monitoring, and many other applications [1].

Federated learning (FL) is a proposed approach to Machine Learning (ML) in which the model's training is distributed among the users. Each device trains the model using its user's raw data. Because FL only shares new updates of the data to other participants, it is a better proposal to ensure user data privacy than other machine learning models that use a centralized model on which each user sends the collected raw data to train the model [2].

FL gained popularity nearly a decade ago when it was proposed by Google engineers in 2016 as an alternative to centralized models to make word prediction in the Google keyboard. This enhances the user experience. They emphasised the advantages of training ML models without transferring raw data to a central server, which lowers privacy and data threats [3]. In the years that followed, Federated Learning gained greater attention, and new algorithms were put out for a variety of applications, including the Internet of Things [4], medicine [5], and more.

In section II, we introduce the limitations of Federated Learning that need to be addressed by different frameworks. In section III, we analyze different algorithms and

techniques associated with Federated Learning, how each algorithm makes predictions, and how each algorithm ensures users' privacy. In section IV, we compare and analyze which technique works more efficiently with Federated Learning and which methodology gives better accuracy.

# II. FEDERATED LEARNING

In this section, we will introduce a breakdown of Federated Learning, including the limitations that impact the models.

## A. Categorization

FL can be categorized in several ways based on the characteristics of the data: Horizontal FL, on which the clients have the same feature space but different samples; in vertical FL, the clients with the same samples have different feature space, and Federated transfer learning, on which the clients has different feature space and different samples [6].

FL can also be classified regarding the network architecture: Centralized architecture, on which the client model updates are aggregated by a central server, decentralized architecture is a Peer-to-peer network that is used for client collaboration and does not have a central server, and hierarchical, which is a multi-tiered FL architecture, where the model training and aggregation are distributed between tiers. Centralized FL is one of the most common architectures used for HAR thanks to its robustness [2].

## B. Drawbacks

* Accuracy

In [3], the authors compared the accuracy results of an FL algorithm with a centralized algorithm, concluding that the centralized algorithms are more accurate.

* Capacity

In [8], the authors consider that in some cases, the devices cannot have the right computational capability to store and process tons of data in the same device, which will lead to computational overhead and delay in the data sharing [9]. Also, in [10], the authors mention the limitations of battery life that will lead to a loss of communication with the central server.
* Communication

The communication costs are the highest in FL due to decentralized data between participants and the constant updates from the model. Also, consider that not all networks have the same upstream and downstream capabilities [11].

* Non-heterogeneous data

When the data utilized to train the model from a participant is similar and follows the same patterns, it generates redundancy during the training of the model, and it can decrease the efficiency of FL models.

* Non-identically independent distribution (Non-IID) data

Non-IID refers to the data heterogeneity; typically, the participants' data are non-IID. In [3], the authors make a comparison between IID and non-IID accuracy, concluding on the lower accuracy of non-IID data training [12].

### III. APPROACHES

The purpose of this section is to elaborate on the proposed algorithms in three different articles, focusing on how they address the drawbacks of FL in the context of HAR:

* In [3], FedAvg is a proposed framework that accumulates only the necessary parameters. This addresses the challenge of reducing communication costs.

* In [13], the authors propose ProtoHAR, an algorithm that addresses the challenge of non-heterogeneous data. The algorithm aims to tackle drawbacks like slow convergence and communication expenses, experimenting in public datasets.

* In [14], the authors propose FedHAR, an approach to handling privacy preservation, real-time processing, and data heterogeneity, using unsupervised gradients and testing it with HAR datasets.

* All these approaches have a horizontal FL characteristic as they align with the challenges of non-IID data distribution across clients, where data on each device is based on the individual usage of users.

#### A. Federated Averaging

Softmax Regression and Deep Neural Network (DNN) are the machine learning models taken into consideration. Given the numerous parameters and processing capability, DNN is preferred over Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN). RNN is expensive, but CNN needs more parameters for greater accuracy. The quantity of parameters matters since more parameters mean that the client and server are sharing more information. Since not all of the data can be exchanged with the server, this could result in an invasion of user privacy. Compared to DNN, which contains more hidden layers, softmax regression is a simpler model. The performance of federated learning and centralized models with various data distributions is compared using these two machine learning models [3].

First, the input characteristics are taken into account using the softmax regression. Each feature is given a weight

according to its significance. Each task is given a score, which is then transformed into probabilities at the conclusion. The final forecast will be the one with the highest likelihood. The input properties for DNN are regarded as being identical to those for softmax regression. These characteristics are subsequently transferred to the neural layer. After completing a few computations, these neurons transfer the information to the following layer for additional processing. The process of merging the features with further intricate patterns is carried out by the next layers. Using the high-level patterns, the final layer provides a probability for the attributes. The final prediction will be the one with the highest likelihood [3].

* Federated Averaging algorithm (FedAvg)

FedAvg is an aggregation algorithm used for FL, described as a distributed ML framework. The model parameters are aggregated to the global server, which fits into the description of a hierarchical architecture. This lowers the cost of communication and, more significantly, protects the clients' privacy. The local clients receive this global model after it has been changed by the model parameters. Until it converges, this process keeps on. Clients with corrupted data are excluded by setting a local test accuracy threshold. If the client is below the threshold, the data won't be added to the global model, improving accuracy [3].

#### B. Prototype Guided Personalized Federated Learning for Human Activity Recognition (ProtoHAR)

ProtoHAR is a prototype-guided federated learning approach. It involves multiple local clients aggregating their respective prototypes to the global server. This ensures a hierarchical architecture. When the sensor data is diverse, this method works well. In the case of heterogeneous data, this is regarded as the first FL algorithm based on a prototype. The average of the features derived from the sensor data is represented by a prototype. Prototypes are made both locally and globally. The global prototype, also known as prototype aggregation, is the combined form of local prototypes from several clients. The local prototype is made using the feature data that the local clients have received. Once again, the local clients receive this global prototype for further training. The client uses the knowledge of global prototypes to update its features during the local training. To minimise heterogeneity, this guarantees that the local prototypes align with the global prototypes. ProtoHAR separates the classifier and representation. It separates the training into a personal classifier and a global representation. The local models learn from the global prototypes in the global representation. Using the characteristics of its local data, the personal classifier generates predictions. Because the data is generalised, this decoupling guarantees that the model better understands the activities across all devices [13].

#### C. FedHAR for Human Activity Recognition

FedHAR is described as a personalized federated HAR framework where the global server aggregates parameters from multiple devices holding local data. This is one of the definitions of a hierarchical architecture. Heterogeneous data, real-time data adaptation, privacy, and unlabeled data are addressed by FedHAR. Federated Online semi-supervised learning and personalized learning are the two parts of the FedHAR algorithm [14].
* Federated Online semi-supervised learning.

Both labeled and unlabeled data will be available to the clients. Both supervised and unsupervised gradients are calculated for clients with labeled data and unlabeled data, respectively. FedHAR employs consistency training for unlabeled clients, assuming neighbouring sensor data and identifying similar behaviours based on the temporal proximity assumption. An unsupervised gradient aggregation technique is used to combine the supervised and unsupervised gradients. Each unlabeled client is given a unique unsupervised gradient, which is then averaged before being sent to the server to guarantee convergence [14].

* Personalized training (PerFedHAR)

PerFedHAR uses semi-supervised learning to customise FedHAR's global model, increasing client-level accuracy in HAR. It uses both supervised gradients (from labeled data) and unsupervised gradients (from client-specific unlabeled data) to fine-tune individual models. Although customers with distinct patterns experienced little improvement, testing on the RealWorld and HAR-UCI datasets revealed average accuracy gains of about 10% (up to 20% for heterogeneous clients) [14].

## IV. COMPARISON & ANALYSIS

In this section, we will compare the results of the experiments of the algorithms in terms of performance, efficiency, and privacy, introducing the datasets used for each experiment.

### A. Performance

* For FedHAR, the authors compare the results between FedHAR, PerFedHAR, and several proposed algorithms, including FedAvg. For the experiments, they use two datasets: the RealWorld sensor-based heterogeneous dataset collected from 15 clients using smartbands and the HAR-UCI public dataset collected from 30 clients using waist-mounted smartphones for daily living activities. Between FedHAR and PerFedHAR, the second algorithm performs +10% better than FedHAR's prediction performance.

* They use accuracy and F1-score as metrics; also, semi-supervised settings perform better for this approach. For PerFedHAR, the percentage of accuracy improves between 10 and 20% on all metrics, depending on the settings.

Tables 1 and 2 show the comparison of the mentioned results

<table><caption>Table 1. Comparison between FedHAR and PerFedHAR using the RealWorld dataset.</caption><thead><tr><td>Model</td><td>Accuracy</td><td>F1-score</td></tr></thead><tbody><tr><td>FedHAR</td><td>72.09</td><td>69.08</td></tr><tr><td>PerFedHAR</td><td>81.69</td><td>79.98</td></tr></tbody></table>

<table><caption>Table 2. Comparison between FedHAR and PerFedHAR using HAR-UCI dataset.</caption><thead><tr><td>Model</td><td>Accuracy</td><td>F1-score</td></tr></thead><tbody><tr><td>FedHAR</td><td>82.08</td><td>80.90</td></tr><tr><td>PerFedHAR</td><td>92.68</td><td>92.32</td></tr></tbody></table>

In Tables 3 and 4, the results demonstrate a better performance of FedHAR against FedAvg due to the gradient aggregation strategy presented by the authors [14]. Real-world results are lower due to the heterogeneity of data [14].

<table><caption>Table 3. Comparison between FedHAR and FedAvg using the RealWorld dataset.</caption><thead><tr><td>Model</td><td>Accuracy</td><td>F1-score</td></tr></thead><tbody><tr><td>FedHAR</td><td>65.31</td><td>62.77</td></tr><tr><td>FedAvg</td><td>53.40</td><td>46.59</td></tr></tbody></table>

<table><caption>Table 4. Comparison between FedHAR and FedAvg using HAR-UCI dataset.</caption><thead><tr><td>Model</td><td>Accuracy</td><td>F1-score</td></tr></thead><tbody><tr><td>FedHAR</td><td>82.61</td><td>81.62</td></tr><tr><td>FedAvg</td><td>68.97</td><td>63.93</td></tr></tbody></table>

* For ProtoHAR, it also compares with other algorithms, including FedAvg, according to the results, which have better percentages, they measured using accuracy, macro F1, and AUC.

* For the dataset, the authors in [13] use 4 public datasets (USC-HAD, UNIMIB-SHAR, PAMAP2, HARBOX) collected from different clients with different sample rates. The datasets were divided into 70% of the data for training and 30% for testing.

Table 5 denotes the comparison between the results of ProtoHAR and FedAvg. AUC (Area Under the ROC Curve) has better results for our evaluation, followed by Accuracy and Macro F1-score metrics.
<table><caption>Table 5. Comparison between ProtoHAR and FedAvg for AUC results.</caption><thead><tr><th rowspan="2">Model</th><th colspan="4">Dataset</th></tr><tr><th>PAMAP2</th><th>USC-HAD</th><th>UNIMI B-SHA R</th><th>HARB OX</th></tr></thead><tbody><tr><td>ProtoHAR</td><td>97.839</td><td>96.518</td><td>99.618</td><td>99.086</td></tr><tr><td>FedAvg</td><td>93.362</td><td>90.633</td><td>97.950</td><td>91.004</td></tr></tbody></table>

* In optimal or less challenging HAR circumstances, FedAvg provides acceptable results as compared to centralized training; however, in more realistic scenarios with data heterogeneity (Non-IID) and real-time online data streams, the performance deteriorates.

## B. Efficiency

* For FedAvg, it has communication efficiency, which reduces the number of communication rounds [3].

* For FedHAR, the algorithm uses a few number of clients with limited labeled samples, and makes continuous updates [14]. More labeled clients and more labeled samples mean more time to converge, even if you don't fine-tune the model. For communication efficiency, even with communication errors during the transmission of gradients, robustness is demonstrated.

## C. Privacy

* FEDAvg ensures that the sensor's data is retained on the user's device [3].

* ProtoHAR sends only the prototypes instead of raw data to enable the data flow without leaking privacy [13].

* FedHAR implements differential privacy, a technique to prevent data recovery attacks [14].

## D. Security

For FedHAR, in [14], the authors performed security tests, focusing on robustness against security attacks. The tests involved tests against malicious users and a data recovery attack. For the first case was proposed a Byzantine-robust aggregation rule was proposed to defend against malicious users (from 2 to 10 users) using the HAR-UCI dataset; the malicious gradients were generated using a Gaussian distribution. For 4 malicious clients, the accuracy without defenses results are 26.18%, for the main experiment, the accuracy with defenses (Median defense) improves, and the result is 75.66%.

The authors introduce differential privacy (DP) using Laplace noise as the main tool for the data recovery attacks tests. The authors tested using RealWorld and HAR-UCI datasets and added Laplace noise to the gradients of each client. The accuracy and F1-score of FedHAR with DP metrics are slightly lower than those of FedHAR, but the performance is acceptable [14].

## V. CONCLUSIONS

Based on the analysis, we can conclude that FL is a good approach to HAR. Also, FedHAR appears to be the most comprehensive framework in tackling the challenges of the real world. It addresses various critical issues like privacy, real-time processing, and heterogeneity within a single framework. It explicitly measures against malicious clients and data recovery attacks. Also, FedHAR is explicitly designed for processing real-time data, unlike FedAvg and ProtoHAR. It is better equipped to handle the challenges encountered in practical, large-scale HAR deployments on mobile and wearable devices. Furthermore, the personalized fine-tuning step (PerFedHAR) in FedHAR directly addresses the performance degradation issues caused by user-specific behavioral heterogeneity, yielding significant accuracy improvements. The results presented for FedHAR and PerFedHAR show their superiority over the FL methods of FedAvg and ProtoHAR.

## VI. FUTURE WORKS

Future studies could propose a hybrid FL framework combining the advantages of FedHAR (privacy preservation and real-time adaptability) and ProtoHAR (efficient handling of heterogeneous data). A model like this could enhance accuracy, efficiency, and security. This may enhance privacy and security as the ProtoHAR limits the sharing of raw data while inheriting Differential Privacy safeguards from FedHAR. By providing prototypes rather than complete model updates, Real-Time Adaptability lowers communication cost and facilitates online learning, which makes it appropriate for wearable and mobile HAR applications. Also, future frameworks can perform experiments on security to ensure the prevention of malicious attacks.

## VII. REFERENCES

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