Train Me If You Can: Decentralized Learning on the Deep Edge

Costa, Diogo; Costa, Miguel; Pinto, Sandro

doi:10.3390/app12094653

Cited by 9 publications

(5 citation statements)

References 79 publications

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“…All parameters are only updated at the very end of this process. The lightweight stochastic gradient descent (L-SGD) algorithm [79] operates differently, retaining only the partial derivatives necessary for the subsequent layer and directly updating the parameters using the calculated gradients. Thus, it only keeps two layers in memory at any given moment.…”

Section: Hardware and Software Optimizations For Reduced Runtimementioning

confidence: 99%

AIfES: A Next-Generation Edge AI Framework

Wulfert,

Kühnel,

Krupp

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Edge Artificial Intelligence (AI) relies on the integration of Machine Learning (ML) into even the smallest embedded devices, thus enabling local intelligence in real-world applications, e.g. for image or speech processing. Traditional Edge AI frameworks lack important aspects required to keep up with recent and upcoming ML innovations. These aspects include low flexibility concerning the target hardware and limited support for custom hardware accelerator integration. Artificial Intelligence for Embedded Systems Framework (AIfES) has the goal to overcome these challenges faced by traditional edge AI frameworks. In this paper, we give a detailed overview of the architecture of AIfES and the applied design principles. Finally, we compare AIfES with TensorFlow Lite for Microcontrollers (TFLM) on an ARM Cortex-M4-based System-on-Chip (SoC) using fully connected neural networks (FCNNs) and convolutional neural networks (CNNs). AIfES outperforms TFLM in both execution time and memory consumption for the FCNNs. Additionally, using AIfES reduces memory consumption by up to 54 % when using CNNs. Furthermore, we show the performance of AIfES during the training of FCNN as well as CNN and demonstrate the feasibility of training a CNN on a resource-constrained device with a memory usage of slightly more than 100 kB of RAM.

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Section: Hardware and Software Optimizations For Reduced Runtimementioning

confidence: 99%

AIfES: A Next-Generation Edge AI Framework

Wulfert,

Kühnel,

Krupp

et al. 2024

IEEE Trans. Pattern Anal. Mach. Intell.

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“…The end of Moore's Law is pushing ML to shift from cloud to edge [44], especially in the next-generation FMS system. Enabling ways to overcome the challenges of data silos and data sensibility, the collaboratively decentralized privacy-preserving paradigm known as Federated Learning (FL) has attracted particular concern among researchers for UAV-based authentication recently [45,46].…”

Section: Federated Learning Accelerationmentioning

confidence: 99%

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

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“…Typical MLaaS follow a centralized computing paradigm where the client sends its data to the cloud to get the results. However, heavy reliance on the cloud induces unpredictable latency due to network overload which may render ML services useless in real-time scenarios [5], [6]. This combined with rising concerns about data privacy is making ML shift to the edge of the network [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, heavy reliance on the cloud induces unpredictable latency due to network overload which may render ML services useless in real-time scenarios [5], [6]. This combined with rising concerns about data privacy is making ML shift to the edge of the network [5], [6]. If this increases privacy for the user as its data is not transferred over the network, the same does not apply to Service Providers (SPs) as their models would be deployed in untrusted edge devices.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SecureQNN: Introducing a Privacy-Preserving Framework for QNNs at the Deep Edge

Costa,

Gomes,

Cabral

et al. 2023

Communications in Computer and Information Science

Self Cite

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Recent concerns about real-time inference and data privacy are making Machine Learning (ML) shift to the edge. However, training efficient ML models require large-scale datasets not available for typical ML clients. Consequently, the training is usually delegated to specific Service Providers (SP), which are now worried to deploy proprietary ML models on untrusted edge devices. A natural solution to increase the privacy and integrity of ML models comes from Trusted Execution Environments (TEEs), which provide hardware-based security. However, their integration with heavy ML computation remains a challenge. This perspective paper explores the feasibility of leveraging a state-of-the-art TEE technology widely available in modern MCUs (TrustZone-M) to protect the privacy of Quantized Neural Networks (QNNs). We propose a novel framework that traverses the model layer-by-layer and evaluates the number of epochs an attacker requires to build a model with the same accuracy as the target with the information disclosed. The set of layers whose information makes the attacker spend less training effort than the owner training from scratch is protected in an isolated environment, i.e., the secure-world. Our framework will be evaluated in terms of latency and memory footprint for two ANNs built for the CIFAR-10 and Visual Wake Words (VWW) datasets. In this perspective paper, we establish a baseline reference for the results.

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Train Me If You Can: Decentralized Learning on the Deep Edge

Cited by 9 publications

References 79 publications

AIfES: A Next-Generation Edge AI Framework

AIfES: A Next-Generation Edge AI Framework

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

SecureQNN: Introducing a Privacy-Preserving Framework for QNNs at the Deep Edge

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