Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices

Karras, Aristeidis; Karras, Christos; Giotopoulos, Konstantinos C.; Tsolis, Dimitrios; Οικονόμου, Κωνσταντίνος; Sioutas, Spyros

doi:10.1109/seeda-cecnsm57760.2022.9932980

Cited by 14 publications

(9 citation statements)

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“…Each Raspberry Pi is configured with 4 GB of RAM and a 1.5 GHz quad-core processor. Importantly, these Raspberry Pi units are connected to a distinct network, separate from the HPC cluster, to emulate a realistic communication scenario as we have proposed in [89,90].…”

Section: Computational Framework For Iot Model Training and Evaluationmentioning

confidence: 99%

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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In the context of the Internet of Things (IoT), Tiny Machine Learning (TinyML) and Big Data, enhanced by Edge Artificial Intelligence, are essential for effectively managing the extensive data produced by numerous connected devices. Our study introduces a set of TinyML algorithms designed and developed to improve Big Data management in large-scale IoT systems. These algorithms, named TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ, operate together to enhance data processing, storage, and quality control in IoT networks, utilizing the capabilities of Edge AI. In particular, TinyCleanEDF applies federated learning for Edge-based data cleaning and anomaly detection. EdgeClusterML combines reinforcement learning with self-organizing maps for effective data clustering. CompressEdgeML uses neural networks for adaptive data compression. CacheEdgeML employs predictive analytics for smart data caching, and TinyHybridSenseQ concentrates on data quality evaluation and hybrid storage strategies. Our experimental evaluation of the proposed techniques includes executing all the algorithms in various numbers of Raspberry Pi devices ranging from one to ten. The experimental results are promising as we outperform similar methods across various evaluation metrics. Ultimately, we anticipate that the proposed algorithms offer a comprehensive and efficient approach to managing the complexities of IoT, Big Data, and Edge AI.

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Section: Computational Framework For Iot Model Training and Evaluationmentioning

confidence: 99%

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Karras,

Giannaros,

Karras

et al. 2024

Future Internet

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“…Federated learning for autonomous vehicle privacy protection: Federated learning is a distributed machine learning technique that allows models to be trained collaboratively without directly sharing the data [192]. Instead of transmitting individual client data to a central server, the central server sends its model to the clients, and each client trains the model with its own data.…”

Section: Privacy Preservation Techniques In Vehicular Communicationsmentioning

confidence: 99%

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

Giannaros,

Karras,

Theodorakopoulos

et al. 2023

JCP

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Autonomous vehicles (AVs), defined as vehicles capable of navigation and decision-making independent of human intervention, represent a revolutionary advancement in transportation technology. These vehicles operate by synthesizing an array of sophisticated technologies, including sensors, cameras, GPS, radar, light imaging detection and ranging (LiDAR), and advanced computing systems. These components work in concert to accurately perceive the vehicle’s environment, ensuring the capacity to make optimal decisions in real-time. At the heart of AV functionality lies the ability to facilitate intercommunication between vehicles and with critical road infrastructure—a characteristic that, while central to their efficacy, also renders them susceptible to cyber threats. The potential infiltration of these communication channels poses a severe threat, enabling the possibility of personal information theft or the introduction of malicious software that could compromise vehicle safety. This paper offers a comprehensive exploration of the current state of AV technology, particularly examining the intersection of autonomous vehicles and emotional intelligence. We delve into an extensive analysis of recent research on safety lapses and security vulnerabilities in autonomous vehicles, placing specific emphasis on the different types of cyber attacks to which they are susceptible. We further explore the various security solutions that have been proposed and implemented to address these threats. The discussion not only provides an overview of the existing challenges but also presents a pathway toward future research directions. This includes potential advancements in the AV field, the continued refinement of safety measures, and the development of more robust, resilient security mechanisms. Ultimately, this paper seeks to contribute to a deeper understanding of the safety and security landscape of autonomous vehicles, fostering discourse on the intricate balance between technological advancement and security in this rapidly evolving field.

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“…In particular, the event detection scheme that detects the environmental changes in IoT has been considered in [ 2 ]. In the IoT for agriculture, the event detection based on machine learning is considered in [ 3 ]. For the IoT of the inside of a car, the error detection of the equipment system is considered in [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA

Takyu,

Takeda,

Miyamoto

et al. 2024

Sensors

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Low-power wide-area (LPWA) is a communication technology for the IoT that allows low power consumption and long-range communication. Additionally, packet-level index modulation (PLIM) can transmit additional information using multiple frequency channels and time slots. However, in a competitive radio access environment, where multiple sensors autonomously determine packet transmission, packet collisions occur when transmitting the same information. The packet collisions cause a reduction in the throughput. A method has been proposed to design a mapping table that shows the correspondence between indexes and information using a packet collision minimization criterion. However, the effectiveness of this method depends on how the probability of the occurrence of the information to be transmitted is modeled. We propose an environment-aware adaptive data-gathering method that identifies the location of factors affecting sensor information and constructs a model for the probability of the occurrence of sensor information. The packet collision rate of the environment-aware adaptive data-gathering method was clarified through computer simulations and actual experiments on a 429 MHz LPWA. We confirm that the proposed scheme improves the packet collision rate by 15% in the computer simulation and 30% in the experimental evaluation, respectively.

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Peer to Peer Federated Learning: Towards Decentralized Machine Learning on Edge Devices

Cited by 14 publications

References 0 publications

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

TinyML Algorithms for Big Data Management in Large-Scale IoT Systems

Autonomous Vehicles: Sophisticated Attacks, Safety Issues, Challenges, Open Topics, Blockchain, and Future Directions

An Environment-Aware Adaptive Data-Gathering Method for Packet-Level Index Modulation in LPWA

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