# Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence

Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville

► To cite this version:

Antoine Bonneau, Frédéric Le Mouël, Fabien Mieyeville. Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence. Workshop on Simplification, Compression, Efficiency and Frugality for Artificial Intelligence (SCEFA), in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Politecnico di Torino; CENTAI, Sep 2023, Turin, Italy. hal-04250026

HAL Id: hal-04250026

https://hal.science/hal-04250026v1

Submitted on 19 Oct 2023

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Addressing limitations of TinyML approaches for AI-enabled Ambient Intelligence

Position Paper

Antoine Bonneau¹,², Frédéric Le Mouël¹, and Fabien Mieyeville¹,²

¹ Univ Lyon, INSA Lyon, Inria, CITI, EA3720, 69621 Villeurbanne, France
{antoine.bonneau, frederic.le-mouel}@insa-lyon.fr

² Univ Lyon, Université Claude Bernard Lyon 1, INSA Lyon, Ecole Centrale de Lyon, CNRS, Ampère, UMR5005, 69622 Villeurbanne, France
fabien.mieyeville@univ-lyon1.fr

**Abstract.** The integration of Artificial Intelligence (AI) and Ambient Intelligence (AmI) has emerged as a promising approach to creating responsive and contextually aware environments. AmI creates contextually aware environments by seamlessly integrating intelligent technologies, while AI develops algorithms for autonomous learning and decision-making. However, embedding AI within AmI environments faces challenges due to limited resources and energy constraints. While recent research on embedded AI has primarily focused on specific tasks of AmI, our goal is to develop a comprehensive framework encompassing all the necessary components for practical use cases. Through this endeavor, we aim to explore power-aware designs and distributed learning as fundamental approaches to address limited computational resources, energy constraints, and dynamic context variations challenges.

**Keywords:** On-Device Learning · Intermittent Learning · Federated Learning · TinyML · Power Consumption

## 1 Introduction

In the past decade, the fields of Artificial Intelligence (AI) and Ambient Intelligence (AmI) have experienced rapid advancements as two complementary domains. AmI [10] is a paradigm that enables responsive and contextually aware environments by seamlessly integrating intelligent technologies into everyday surroundings. It involves the creation of intuitive systems capable of perceiving, reasoning, and adapting to human presence, needs, and preferences, thereby enhancing user experiences and simplifying interactions with the digital world. AmI has found a natural ally in AI, the domain of computer science concerned with developing intelligent systems capable of performing tasks that typically require human intelligence. It encompasses the development of algorithms and models that enable machines to learn, reason, and make decisions autonomously, mimicking cognitive processes. The intertwining of AI and AmI has been a natural progression [7], as their combined potential holds the promise of unlocking new frontiers in intelligent systems.
How can AmI and AI combination be beneficial? Local data processing and analysis at the deep edge with AI reduces reliance on continuous connectivity and centralized infrastructure, enhancing resilience against network disruptions and limiting personal data exposure. Deploying ML on resource-constrained sensor bring intelligence into AmI hardware. Even though entirely autonomous systems are still out of reach, working on infrastructural bricks is essential in developing AmI's ubiquitous network and can benefit Wireless Sensors Networks Monitoring (WSN) applications. Installing sensors within agricultural fields [18] can facilitate optimized irrigation, fertilization, and pest control. Similarly, in marine ecosystems [35], WSN could help monitor water quality or detect harmful algal blooms [15]. Critical civilian infrastructure elements are prone to damage resulting from environmental factors and operational conditions. The domain of Structural Health Monitoring (SHM) [12] could improve the detection of anomalies or strains by enabling WSN to locally analyze this data, promptly identifying potential issues, and triggering timely maintenance or repairs. In pursuing knowledge and exploring remote and extreme environments like deep space [29], humanity requires safe camps; hence, these isolated habitats can yield substantial benefits to AmI to monitor environmental conditions and resource usage and ensure the safety of explorers.

The integration of AI and AmI presents several challenges. AmI, being inherently integrated into the environment, necessitates miniaturized hardware, thereby imposing constraints on computational resources and autonomy because of the reduced possible size of the battery. Embedded devices, including micro-controller units (MCUs) and sensors, have limited processing power and memory. In the meantime, AI algorithms, particularly Machine Learning (ML) ones, tend to be computationally demanding. Energy consumption is critical for energy-harvesting (EH) and battery-powered devices. The typical efficiencies of energy harvesting techniques suitable for AmI devices range from $10^{-5}$ to $10^{-2}$ W [34], while the power consumption of these devices during operational tasks necessary for performing computations, is on the order of $10^{-1}$ W. Nevertheless, the nodes can operate despite an unfavorable ratio, as they spend a significant portion of their lifespan in standby mode. Few studies look at power consumption, and even fewer take physical measurements, preferring estimates based on datasheets [31], and the few that do only measure microcontroller [23] power consumption neglecting the rest of the hardware. ML models, especially Deep Learning (DL) models, can be large and complex, requiring significant memory and storage resources severely limited in embedded devices. AmI environments are dynamic and subject to context variations. Thus, AI models deployed on embedded devices should be adaptable to such variations and robust enough to handle uncertainties and noisy sensor data.

To address resource limitations in embedding AI within AmI environments, we propose combining Federated Learning and Intermittent Learning with TinyML. Federated Learning enables collaborative model training and resource sharing across multiple devices. Discontinuously powered devices require Intermittent Learning to be usable. When integrated with TinyML, these approaches enable lightweight and efficient deployment of machine learning models on embedded devices, ensuring privacy and adaptability in AmI settings.
# 2 Background and Motivations

## 2.1 Current implementation of Ambient intelligence

The integration of sensors and AI has transformed computing. Initially, sensors relied on rule-based frameworks but have now advanced to ML-based approaches. They have transitioned from plugged-into power sources to mobile with energy harvesting or charging stations. For instance, researchers develop wearable health monitoring devices powered through energy harvesting for continuous operation, while autonomous vacuum cleaners recharge at dedicated stations.

Semantic AI is a branch of AI that focuses on understanding and interpreting the meaning of data and information. It involves using rules and algorithms to process and analyze data to make intelligent decisions and take appropriate actions. AmI implementations traditionally leveraged this AI to emulate contextual intelligence [22]. For instance, intelligent agents equipped with semantic knowledge can autonomously control home appliances. In healthcare, Semantic AI is being utilized to assist individuals in their daily activities, such as reminding them to take medication.

ML-based AI [10] has improved the performance and complexity of tasks previously performed with semantic AI and introduced new possibilities such as language, gesture, emotion, and human activity recognition. Current models are mostly trained using supervised or reinforcement techniques. Nevertheless, rule-based systems or supervised learning lack the necessary robustness to effectively adapt to a continuously evolving real-world environment that deviates from its initial design and deployment conditions.

## 2.2 Energy consumption concerns in TinyML

The essence of intelligent sensors lies in their ability to learn, which is now facilitated by TinyML techniques [11,25]. On-device learning empowers the local processing of information, primarily focusing on inference tasks, following the refinement of a pre-trained model [37]. On the other hand, autonomous systems and EH technologies enable self-powered AmI applications but imply a tight energy budget and have to cope with inevitable power shortages. The energy storage supporting EH [27] is a challenge in itself: batteries have higher energy density and provide constant (but relatively high) voltage, while supercapacitors have low energy density and provide unstable (but relatively low) voltage. On the other hand, supercapacitors are exposed to significant depletion, while batteries have only a few charge/discharge cycles. Due to the compact size of the sensors, the batteries employed must be small, typically ranging from 600 to 700 mAh [4]. Consequently, these batteries can only sustain an always-on device's requirements on a daily timescale or monthly, considering periods of sleep.

Previous works have shown that it is possible to use advanced AI solutions despite the memory constraints of low-end devices [20,25]. Lin et al. [17] has successfully fine-tuned a human recognition model on an STM32F746, a 32 bits microcontroller typical of low-end devices using less than 256 KB of RAM and 1 MB of Flash. Sudharsan et al. [31,32] employed smaller models and trained them from scratch using
a reformable offline approach [24] on low-end MCUs. However, their approach solely relied on the datasheet information to estimate the computational requirements for training and inference processes. Profentzas et al. [23] optimized a pre-trained activity-recognition neural network on an nRF-52840 SoC featuring: a 32-bit ARM Cortex-M4 with FPU at 64 MHz, 256 KB of RAM and 1 MB of Flash physically measuring energy consumption. However, they limited their instrumentation to the MCU and did not reflect the actual energy-guzzling components consumption of a board such as the voltage regulator.

## 3 Concrete Use Cases

When establishing a cloud link for WSN proves to be arduous, cloud processing of ML models may be impossible. In certain circumstances, monitoring applications assume criticality while simultaneously encountering challenges in terms of connectivity. The accessibility of these environments to human presence may present challenges or render them permanently inaccessible once the devices are activated.

*Environmental Monitoring* Utilizing ML on sensors within agricultural fields [18] can facilitate optimized irrigation, fertilization, and pest control. Similarly, in marine ecosystems [35], ambient intelligence implemented in a WSN would help monitor water quality, tidal behavior changes, detect harmful algal blooms, and contribute to conservation efforts. Civil Engineering critical infrastructure elements such as bridges and dams are prone to potential damage from various environmental factors, operational conditions, and daily interactions with multiple agents. This is the domain of Structural Health Monitoring (SHM) [12].

*Habitats Optimized for Missions of Exploration* In the pursuit of knowledge and exploration of remote and extreme environments like deep space [29], or ocean depths, humanity requires safe camp infrastructure to accomplish missions. By deploying sensor networks, these habitats can be monitored for environmental conditions, resource usage, and the safety of explorers. Embedded intelligence enables local analysis on the sensor nodes, facilitating anomaly identification and timely alerts. This proactive approach ensures the success of exploration missions and the well-being of personnel in challenging environments.

## 4 Challenges and Elements of a conceptual framework

In the realm of embedded systems, limited memory and power outages pose significant challenges. We want to develop a cross-platform framework departing from a lightweight RTOS addressing presented issues. We want to compare low-level programming ease, relevant frameworks [21,14], and microcontroller operating systems [1] to analyze associated overhead. To facilitate comprehensive evaluations, we propose assessing energy consumption in MCU boards within the context of AmI by focusing on individual tasks that can be examined independently. We will evaluate data acquisition and cleaning processes (section 4.1), analyze trade-offs of ML models
for classification and regression in a power-aware context (section 4.2), and assess
the energy consumption of various communication protocols and distributed learning
 subsection 4.3).

4.1 Minimizing hardware requirements through Data Reduction

Employing data reduction techniques can decrease the volume of manipulated data by selectively extracting helpful information from input signals, thereby mitigating memory constraints. A consequential advantage of data reduction is the reduction in processing time needed for analysis or computation.

*Adaptive Sampling* [33,28] entails the dynamic selection of representative data points from large datasets, which efficiently utilizes computational resources. The computational burden is significantly reduced by intelligently sampling only the most informative data instances. Moreover, adaptive sampling reduces the overall time required for model training and inference, enabling time-sensitive applications to operate effectively within the limitations of embedded systems.

*TimeSeries Segmentation* [6,19,13] involves partitioning time-varying data into smaller, more manageable segments based on characteristic patterns and behaviors. This technique enables the selective analysis of specific segments rather than processing the entire time series data. The segmentation or *change point detection* methods are similar to feature extraction and would help focus on relevant portions without requiring a demanding Convolutional Neural Network (CNN).

4.2 Meeting short and long-term time constraints

One significant resulting challenge of EH systems is the occurrence of power shortages that may hinder continuous operation. By carefully considering power availability and adapting system behavior accordingly, embedded computing can effectively operate within energy-limited environments. Limited device accessibility and updates make it imperative to imbue devices with self-assessment capabilities during the design stage. This challenge entails enabling devices to evaluate and analyze their understanding of the environment. By incorporating self-assessment and adaptation mechanisms, devices can autonomously maintain and update their understanding of the environment, ensuring effective operation even in dynamic or disrupting circumstances.

*Intermittent Learning* refers to a learning paradigm that manages irregular or sporadic availability of training data or computational resources. Lee et al. [16] introduced *Intermittent Learning* as a way for EH computing platforms to perform machine learning tasks effectively and efficiently. Among the frameworks for intermittent learning that later emerged, [2] developed *REHASH*, a tool helping design adaptive IoT workflow heuristics. They evaluated their work by running activity recognition and greenhouse monitoring applications using an MSP430FR5994 Launchpad.
*Incremental/Continual Learning* refers to a machine learning paradigm that focuses on the ability of a model to learn from new data incrementally over time without requiring retraining from scratch [30]. Ren et al. [26] have tested their incremental online learning algorithm TinyOL on an Arduino 33 BLE board in a default detection application for USB fan vibrations data. Continual learning involves updating and expanding the model's knowledge while preserving previously learned information, enabling adaptation to new tasks or concepts without significant performance degradation. Until recent advances, retraining a model that has experienced concept drift in its evaluated data entails transmitting the data through the internet to a server responsible for generating a new model [24] subsequently redistributed to the hardware executing inferences.

## 4.3 Enabling devices collaboration using Federated Learning

Federated Learning (FL) addresses the challenge of distributed data storage by allowing entities to collaboratively train models without sharing their data. FL is classified into two categories: Centralized Federated Learning (CFL) and Decentralized/Distributed/Serverless Federated Learning (DFL) [3,36]. CFL uses a central server, while DFL enables decentralized aggregation of model parameters. DFL finds significant utility in overcoming AmI device constraints disconnected from the internet and centralized servers. In such scenarios, DFL serves as a means to overcome these individual deficiencies by leveraging the collective power of the network. DFL allows for collaboratively training models and achieving higher intelligence without relying on solid individual capabilities. This collective approach could virtually circumvent computational weaknesses and memory constraints, providing a robust solution for AmI systems if in-situ and collaborative processing are balanced enough to justify communication overhead.

Some works have already started addressing the constraints imposed on intelligent sensors and the distribution of AI calculations in embedded systems. Dai et al. [9], with *DispFL*, have proposed to act on communication costs using personalized FL and decentralized learning. Costa et al. [8] designed a framework to enable the deployment of decentralized learning in resource-constrained devices. They evaluated their contribution on a STM32L4R5ZIT6 MCU over some Artificial Neural Network models.

In the context of AmI, the implementation of FL necessitates addressing the inherent challenges posed by the fluctuating size of the learning network and the varying availability of energy resources among participants. Learning occurs exclusively among active nodes, which may vary across iterations. Hence, adopting specific strategies to ensure the FL process's reliability and expeditious convergence is imperative.

# 5 Conclusion and Future Work

We claim that the challenges linked to incorporating AI into Ambient Intelligence (AmI) settings can be successfully resolved by integrating Federated Learning, Intermittent Learning, and TinyML [5]. Combining those approaches and data reduction
techniques enables collaborative model training and resource sharing across devices while addressing the sporadic availability of training data and computational power. Overall, this approach holds great potential to enhance the performance and resilience of embedded systems in resource-constrained environments, enabling the advancement of intelligent systems in AmI and unlocking new possibilities. We will evaluate solutions on a testbed composed of piezoelectric patches on an aluminum plate. TinyML sensors must collect and analyze data to identify vibration sources and distance and evaluate default type and severity. We then want to focus on distributed learning strategies for SHM tasks with separate processing nodes for piezoelectric signals.
References

1. Baccelli, E., Gundogan, C., Hahm, O., Kietzmann, P., Lenders, M.S., Petersen, H., Schleiser, K., Schmidt, T.C., Wahlisch, M.: RIOT: An open source operating system for low-end embedded devices in the IoT. IEEE Internet of Things Journal **5** (Dec 2018). doi: 10.1109/JIOT.2018.2815038

2. Bakar, A., Ross, A.G., Yildirim, K.S., Hester, J.: REHASH: A Flexible, Developer Focused, Heuristic Adaptation Platform for Intermittently Powered Computing. In: ACM Interactive, Mobile, Wearable and Ubiq. Tech. (Sep 2021). doi: 10.1145/3478077

3. Beltrán, E.T.M., Pérez, M.Q., Sánchez, P.M.S., Bernal, S.L., Bovet, G., Pérez, M.G., Pérez, G.M., Celdrán, A.H.: Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges (Nov 2022). doi: 10.48550/arXiv.2211.08413

4. Blaauw, D., Sylvester, D., Dutta, P., Lee, Y., Lee, I., Bang, S., Kim, Y., Kim, G., Pannuto, P., Kuo, Y.S., Yoon, D., Jung, W., Foo, Z., Chen, Y.P., Oh, S., Jeong, S., Choi, M.: IoT design space challenges: Circuits and systems. In: Symposium on VLSI Technology : Digest of Technical Papers (Jun 2014). doi: 10.1109/VLSIT.2014.6894411

5. Bonneau, A., Le Mouël, F., Mieyeville, F.: Energy-efficient in-situ monitoring using on-device and distributed learning. In: Green Days. Lyon, France (Mar 2023)

6. Chiappa, S.: A Bayesian Approach to Switching Linear Gaussian State-Space Models for Unsupervised Time-Series Segmentation. In: 7th ICML. IEEE (2008). doi: 10.1109/ICMLA.2008.109

7. Chin, J., Callaghan, V., Allouch, S.B.: The Internet-of-Things: Reflections on the past, present and future from a user-centered and smart environment perspective. Journal of Ambient Intelligence and Smart Environments **11** (Jan 2019). doi: 10.3233/AIS-180506

8. Costa, D., Costa, M., Pinto, S.: Train Me If You Can: Decentralized Learning on the Deep Edge. Applied Sciences **12**(9) (May 2022). doi: 10.3390/app12094653

9. Dai, R., Shen, L., He, F., Tian, X., Tao, D.: DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse Training (May 2022). doi: 10.48550/arXiv.2206.00187

10. Dunne, R., Morris, T., Harper, S.: A survey of ambient intelligence. ACM Computing Surveys **54** (May 2022). doi: 10.1145/3447242

11. Dutta, D.L., Bharali, S.: TinyML Meets IoT: A Comprehensive Survey. Internet of Things **16** (Dec 2021). doi: 10.1016/j.iot.2021.100461

12. Flah, M., Nunez, I., Ben Chaabene, W., Nehdi, M.L.: Machine Learning Algorithms in Civil Structural Health Monitoring: A Systematic Review. Archives of Computational Methods in Engineering **28** (Jun 2021). doi: 10.1007/s11831-020-09471-9

13. Gsior, K., Urbaska, H., Grzesiek, A., Zimroz, R., Wyomaska, A.: Identification, Decomposition and Segmentation of Impulsive Vibration Signals with Deterministic Components : A Sieving Screen Case Study. Sensors **20** (Oct 2020). doi: 10.3390/s20195648

14. Han, H., Siebert, J.: TinyML: A Systematic Review and Synthesis of Existing Research. In: ICAIIC (Feb 2022). doi: 10.1109/ICAIIC54071.2022.9722636

15. Khan, R.M., Salehi, B., Mahdianpari, M., Mohammadimanesh, F., Mountrakis, G., Quackenbush, L.J.: A Meta-Analysis on Harmful Algal Bloom Detection and Monitoring: A Remote Sensing Perspective. Remote Sensing **13** (Jan 2021). doi: 10.3390/rs13214347

16. Lee, S., Islam, B., Luo, Y., Nirjon, S.: Intermittent Learning: On-Device Machine Learning on Intermittently Powered System. In: ACM Interactive, Mobile, Wearable and Ubiq. Tech. (Dec 2019). doi: 10.1145/3369837

17. Lin, J., Zhu, L., Chen, W.M., Wang, W.C., Gan, C., Han, S.: On-Device Training Under 256KB Memory. In: ANIPS. vol. 35 (Jul 2022). doi: 10.48550/arXiv.2206.15472
18. Mohinur Rahaman, M., Azharuddin, M.: Wireless sensor networks in agriculture through machine learning: A survey. Computers and Electronics in Agriculture **197** (Jun 2022). doi: 10.1016/j.compag.2022.106928

19. Monte, G., Huang, V., Liscovsky, P., Marasco, D., Agnello, A.: Standard of things, first step: Understanding and normalizing sensor signals. In: IECON (Nov 2013). doi: 10.1109/IECON.2013.6699121

20. Ojo, M.O., Giordano, S., Procissi, G., Seitanidis, I.N.: A review of low-end, middle-end, and high-end iot devices. IEEE Access **6** (2018). doi: 10.1109/ACCESS.2018.2879615

21. Osman, A., Abid, U., Gemma, L., Perotto, M., Brunelli, D.: TinyML platforms benchmarking (Nov 2021). doi: 10.48550/arXiv.2112.01319

22. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the internet of things: A survey. IEEE Communications Surveys & Tutorials **16** (2014). doi: 10.1109/SURV.2013.042313.00197

23. Profentzas, C., Almgren, M., Landsiedel, O.: MiniLearn: On-device learning for low-power IoT devices. In: EWSN (2022). doi: 10.5555/3578948.3578949

24. Rajapakse, V., Karunanayake, I., Ahmed, N.: Intelligence at the Extreme Edge: A Survey on Reformable TinyML. ACM Computing Surveys (Feb 2023). doi: 10.1145/3583683

25. Ray, P.P.: A review on TinyML: State-of-the-art and prospects. Journal of King Saud University - Computer and Information Sciences **34** (Apr 2022). doi: 10.1016/j.jksuci.2021.11.019

26. Ren, H., Anicic, D., Runkler, T.A.: TinyOL: TinyML with online-learning on microcontrollers. In: IJCNN (Jul 2021). doi: 10.1109/IJCNN52387.2021.9533927

27. Riaz, A., Sarker, M.R., Saad, M.H.M., Mohamed, R.: Review on comparison of different energy storage technologies used in micro-energy harvesting, WSNs, low-cost microelectronic devices: Challenges and recommendations. Sensors **21** (Jan 2021). doi: 10.3390/s21155041

28. Rodriguez-Pabon, C., Riva, G., Zerbini, C., Ruiz-Rosero, J., Ramirez-Gonzalez, G., Corrales, J.C.: An Adaptive Sampling Period Approach for Management of IoT Energy Consumption: Case Study Approach. Sensors **22** (Feb 2022). doi: 10.3390/s22041472

29. Rollock, A.E., Klaus, D.M.: Defining and characterizing self-awareness and self-sufficiency for deep space habitats. Acta Astronautica **198** (Sep 2022). doi: 10.1016/j.actaastro.2022.06.002

30. Shaheen, K., Hanif, M.A., Hasan, O., Shafique, M.: Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks (May 2021). doi: 10.48550/arXiv.2105.12374

31. Sudharsan, B., Breslin, J.G., Ali, M.I.: Edge2Train: a framework to train machine learning models (SVMs) on resource-constrained IoT edge devices. In: 10th Intl. Conference on the Internet of Things (Oct 2020). doi: 10.1145/3410992.3411014

32. Sudharsan, B., Salerno, S., Nguyen, D.D., Yahya, M., Wahid, A., Yadav, P., Breslin, J.G., Ali, M.I.: TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers. In: IEEE 7th World Forum on Internet of Things (WF-IoT) (Jun 2021). doi: 10.1109/WF-IoT51360.2021.9595024

33. Trihinas, D., Pallis, G., Dikaiakos, M.D.: AdaM: An adaptive monitoring framework for sampling and filtering on IoT devices. In: 2015 IEEE International Conference on Big Data (Oct 2015). doi: 10.1109/BigData.2015.7363816

34. Williams, A.J., et al.: Survey of energy harvesting technologies for wireless sensor networks. IEEE Access (2021). doi: 10.1109/ACCESS.2021.3083697

35. Xu, G., Shen, W., Wang, X.: Applications of Wireless Sensor Networks in Marine Environment Monitoring: A Survey. Sensors **14** (Sep 2014). doi: 10.3390/s140916932

36. Yuan, L., Sun, L., Yu, P.S., Wang, Z.: Decentralized federated learning: A survey and perspective (Jun 2023). doi: 10.48550/arXiv.2306.01603