arXiv subjects

Julien Gascon-Samson

Publications and source records attributed to Julien Gascon-Samson.

3 recordsLinked to original sources

arXiv2026

BlazeAIoT: A Modular Multi-Layer Platform for Real-Time Distributed Robotics Across Edge, Fog, and Cloud Infrastructures

The increasing complexity of distributed robotics has driven the need for platforms that seamlessly integrate edge, fog, and cloud computing layers while meeting strict real-time constraints. This paper introduces BlazeAIoT, a modular multi-layer platform designed to unify distributed robotics across heterogeneous infrastructures. BlazeAIoT provides dynamic data transfer, configurable services, and integrated monitoring, while ensuring resilience, security, and programming language flexibility. The architecture leverages Kubernetes-based clusters, broker interoperability (DDS, Kafka, Redis, and ROS2), and adaptive data distribution mechanisms to optimize communication and computation across diverse environments. The proposed solution includes a multi-layer configuration service, dynamic and adaptive data bridging, and hierarchical rate limiting to handle large messages. The platform is validated through robotics scenarios involving navigation and artificial intelligence-driven large-scale message processing, demonstrating robust performance under real-time constraints. Results highlight BlazeAIoT's ability to dynamically allocate services across incomplete topologies, maintain system health, and minimize latency, making it a cost-aware, scalable solution for robotics and broader IoT applications, such as smart cities and smart factories.

cs.RO↗

arXiv2025

Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence

Federated learning has become a promising distributed learning concept with extra insurance on data privacy. Extensive studies on various models of Federated learning have been done since the coinage of its term. One of the important derivatives of federated learning is hierarchical semi-decentralized federated learning, which distributes the load of the aggregation task over multiple nodes and parallelizes the aggregation workload at the breadth of each level of the hierarchy. Various methods have also been proposed to perform inter-cluster and intra-cluster aggregation optimally. Most of the solutions, nonetheless, require monitoring the nodes' performance and resource consumption at each round, which necessitates frequently exchanging systematic data. To optimally perform distributed aggregation in SDFL with minimal reliance on systematic data, we propose Flag-Swap, a Particle Swarm Optimization (PSO) method that optimizes the aggregation placement according only to the processing delay. Our simulation results show that PSO-based placement can find the optimal placement relatively fast, even in scenarios with many clients as candidates for aggregation. Our real-world docker-based implementation of Flag-Swap over the recently emerged FL framework shows superior performance compared to black-box-based deterministic placement strategies, with about 43% minutes faster than random placement, and 32% minutes faster than uniform placement, in terms of total processing time.

cs.DC↗

arXiv2025

SDFLMQ: A Semi-Decentralized Federated Learning Framework over MQTT

Federated Learning is widely discussed as a distributed machine learning concept with stress on preserving data privacy. Various structures of Federated Learning were proposed. Centralized Federated learning for instance has been the primary structure that suits cloud computing. Decentralized Federated learning also has been proposed for ecosystems where communication is dominantly peer-to-peer. Semi-Decentralized Federated Learning (SDFL) has emerged recently as a new concept where the interconnected nodes are clustered, and each cluster is managed independently. The potential of SDFL lies in its clustering feature, which distributes the load of the global model update down onto multiple nodes. Since the concept is fairly new, much can be done to render this FL model a reliable, efficient, and real-time service at the edge. In this paper, we propose SDFLMQ, a semi-decentralized Federated learning framework at the Edge that uses MQTT as the communication protocol. We demonstrate how the publish/subscribe communication model is used to facilitate the clustering and load balancing in SDFL. We also demonstrate how SDFLMQ can use some of the core MQTT features to expand its capacity with no significant costs. Based on some primary evaluations, we demonstrate how SDFLMQ can efficiently distribute the load of aggregation, and potentially save unnecessary memory allocation, all with no requirement for a powerful central unit for aggregation and global model update. We also disclose some of the key future expansions of SDFLMQ with a focus on the operation of large deep neural network models at the edge.

cs.DC↗