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TinyReptile: TinyML with Federated Meta-Learning

TinyReptile: TinyML with Federated Meta-Learning

引用

International Joint conference on Neural networks (IJCNN)

作者： Ren, Haoyu Anicic, Darko Runkler, Thomas A. Siemens AG Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9781665488679

Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can benefit from aggregating their knowledge. Federated learning (FL) enables decentralized agents to jointly learn a global model without sharing sensitive local data. However, a common global model may not work for all devices due to the complexity of the actual deployment environment and the heterogeneity of the data available on each device. In addition, the deployment of TinyML hardware has significant computational and communication constraints, which traditional ML fails to address. Considering these challenges, we propose TinyReptile, a simple but efficient algorithm inspired by meta-learning and online learning, to collaboratively learn a solid initialization for a neural network (NN) across tiny devices that can be quickly adapted to a new device with respect to its data. We demonstrate TinyReptile on Raspberry Pi 4 and Cortex-M4 MCU with only 256-KB RAM. The evaluations on various TinyML use cases confirm a resource reduction and training time saving by at least two factors compared with baseline algorithms with comparable performance.

关键词： Tiny Machine Learning Meta-Learning Federated Learning Online Learning Neural networks internet of Things Microcontroller.

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Design and Evaluation of Deep Reinforcement Learning for Energy Saving in Open RAN 22

Design and Evaluation of Deep Reinforcement Learning for Ene...

引用

22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Bordin, Matteo Lacava, Andrea Polese, Michele Satish, Sai Nittoor, Manoj AnanthaSwamy Sivaraj, Rajarajan Cuomo, Francesca Melodia, Tommaso Institute for the Wireless Internet of Things Northeastern University BostonMA United States Sapienza University of Rome Rome Italy Mavenir RichardsonTX United States

ISBN: (纸本)9798331508050

Next-generation wireless systems, already widely deployed, are expected to become even more prevalent in the future, representing challenges in both environmental and economic terms. This paper focuses on improving the energy efficiency of intelligent and programmable Open Radio Access network (RAN) systems through the near-real-time dynamic activation and deactivation of Base Station (BS) Radio Frequency (RF) frontends using Deep Reinforcement Learning (DRL) algorithms, i.e., Proximal Policy Optimization (PPO) and Deep Q-network (DQN). These algorithms run on the RAN Intelligent controllers (RICs), part of the Open RAN architecture, and are designed to make optimal network-level decisions based on historical data without compromising stability and performance. We leverage a rich set of Key performance Measurements (KPMs), serving as state for the DRL, to create a comprehensive representation of the RAN, alongside a set of actions that correspond to some control exercised on the RF frontend. We extend ns-O-RAN, an open-source, realistic simulator for 5G and Open RAN built on ns-3, to conduct an extensive data collection campaign. This enables us to train the agents offline with over 300,000 data points and subsequently evaluate the performance of the trained models. Results show that DRL agents improve energy efficiency by adapting to network conditions while minimally impacting the user experience. Additionally, we explore the trade-off between throughput and energy consumption offered by different DRL agent designs. © 2025 IEEE.

关键词： Energy efficiency

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Bridging the Chasm Between Ideal and Realistic Federated Learning: A Measurements Study 14

Bridging the Chasm Between Ideal and Realistic Federated Lea...

引用

14th IEEE International conference on Cloud Computing Technology and Science (CloudCom)

作者： Gedawy, Hend K. Harras, Khaled A. Tanveer, Temoor Bui, Thang Carnegie Mellon Univ Comp Sci Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9798350339826

Federated Learning is being hailed as a privacy-preserving machine learning alternative, by allowing models to be distributively trained on source devices owning their data. Most FL solutions, and their assessments, however, assume superior environmental reliability, despite the more realistic variances in environmental factors such as device and network capacity, data distribution, and device churn. As such, we argue in this paper, that there is a growing chasm between current FL assessment setups and the evolving FL assessment needs. Motivated by this chasm, we conduct, to the best of our knowledge, the first empirical measurement study of FL performance given realistic environmental factors. Our study quantifies the impact of these environmental factors on FL performance in terms of training time, accuracy, and communication overhead. Our findings have broad implications for the future development of FL including client admission control and scheduling optimizations.

关键词： Measurements Federated Learning systems Edge Computing internet of Things

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Demo: Enabling Deep Reinforcement Learning Research for Energy Saving in Open RAN 22

Demo: Enabling Deep Reinforcement Learning Research for Ener...

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22nd IEEE Consumer Communications and networking conference, CCNC 2025

作者： Bordin, Matteo Lacava, Andrea Polese, Michele Cuomo, Francesca Melodia, Tommaso Institute for the Wireless Internet of Things Northeastern University BostonMA United States Sapienza University of Rome Rome Italy

ISBN: (纸本)9798331508050

The growing performance demands and higher deployment densities of next-generation wireless systems emphasize the importance of adopting strategies to manage the energy efficiency of mobile networks. In this demo, we showcase a framework that enables research on Deep Reinforcement Learning (DRL) techniques for improving the energy efficiency of intelligent and programmable Open Radio Access network (RAN) systems. Using the open-source simulator ns-O-RAN and the reinforcement learning environment Gymnasium, the framework enables to train and evaluate DRL agents that dynamically control the activation and deactivation of cells in a 5G network. We show how to collect data for training and evaluate the impact of DRL on energy efficiency in a realistic 5G network scenario, including users' mobility and handovers, a full protocol stack, and 3rd Generation Partnership Project (3GPP)-compliant channel models. The tool will be open-sourced upon acceptance of this paper and a tutorial for energy efficiency testing in ns-O-RAN. © 2025 IEEE.

关键词： Deep reinforcement learning

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Joint Computation and Communication Resources Allocation in Industrial internet of Things with Edge Computing 13

Joint Computation and Communication Resources Allocation in ...

引用

13th IEEE International conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Sun, Mingyue Lian, Zhenghang Yuan, Yazhou Ma, Kai Luo, Xiaoyuan Institute of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

As the scale and functional capabilities of Industrial internet of Things (iioT) deployments continue to expand, the amount of data at the device end on the field site is growing exponentially, which leads to unreliable communication quality and limited network resources. To improve the performance of the communication network, this paper proposes a cloud-edge-end three-layer communication architecture that integrates edge computing technology (ECT) with cooperative communication technology. Edge servers (ES) with computing capabilities are deployed in the factory as data forwarding relays to reduce the packet loss rate of the communication network. Under the constraints of communication resources, a bandwidth release model is established to analyze the mapping relationship between computing power and bandwidth. In addition, we adopt the Stackelberg game to characterize the interaction between the factory and the ES, and construct an optimization problem that maximizes the factory utility under the constraint of the total amount of network resources. Finally, a distributed iterative algorithm based on the backward induction method is used to achieve the joint optimization allocation of computing power and bandwidth. Simulation results demonstrate that the proposed solution can effectively allocate resources and improve the overall efficiency of the factory. © 2023 IEEE.

关键词： Bandwidth

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Model-Mediated Delay Compensation with Goal Prediction for Robot Teleoperation Over internet

Model-Mediated Delay Compensation with Goal Prediction for R...

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2023 IEEE International conference on systems, Man, and Cybernetics, SMC 2023

作者： Lima, Rolif Vakharia, Vismay Rai, Utsav Mehta, Hardik Vatsal, Vighnesh Das, Kaushik Tcs Research & Innovation Tata Consultancy Services Bengaluru India

ISBN: (纸本)9798350337020

Teleoperated robots have enabled humans to manipulate objects in remote environments without requiring physical presence. In this paper we focus on teleoperation of a robotic arm with shared control between the robot and the operator. A model-mediated approach is used to compensate for delays in the communication channel. Position information of the operator's arm is captured and processed to compute the states of a motion prediction model before transmission over a network to be used on the robot's side, allowing for compensation of transmission delays. Model Predictive control (MPC) and a novel goal prediction algorithm is used to follow the operator's intended motion while reducing the cognitive loads arising from collision avoidance and fine manipulation in the remote environment. We evaluate the proposed method against a baseline pure teleoperation condition with an inverse kinematic controller and observe that the proposed approach improves the overall teleoperation performance in terms of task completion time. © 2023 IEEE.

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Novel Position Falsification Attacks Detection in the internet of Vehicles using Machine Learning 17

Novel Position Falsification Attacks Detection in the Intern...

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17th International conference on control, Automation, Robotics and Vision (ICARCV)

作者： Ilango, Harun Surej Ma, Maode Su, Rong Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Qatar Univ Coll Engn Doha 2713 Qatar

ISBN: (纸本)9781665476874

In an internet of Vehicles (IoV) network, vehicles periodically broadcast Basic Safety Messages (BSMs) that contain the vehicle's current position, speed, and acceleration. Safety-critical applications like blind-spot warning and lane change warning systems use these BSMs to ensure the safety of road users. However, an attacker can affect the efficacy of such applications by injecting false information into the messages. One such attack is the position falsification attack, where the attacker inserts incorrect information regarding the vehicle's position in the BSMs. The literature has explored the use of Misbehavior Detection systems (MDSs) to detect position falsification attacks. But the limitation of the existing MDSs is that they are signature-based and require prior knowledge about the attacks for effective detection. To overcome this shortcoming, we propose a Novel Position Falsification Attack Detection System for the internet of Vehicles (NPFADS for the IoV) that learns and detects new position falsification attacks emerging in IoV networks. The performance of NPFADS is quantitatively measured using the metrics precision, recall, F1 score, and ROC. The Vehicular Reference Misbehavior (VeReMi) dataset is used as the benchmark to analyze the performance of NPFADS. The performance of NPFADS is compared to existing MDSs in the literature, and the analysis shows that NPFADS performs on par with the existing signature-based detection models even when initialized with zero initial knowledge.

关键词： Vehicles

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A Summary of Rotational Speed Measurement Techniques for Bearing and Rotor systems—Part I

A Summary of Rotational Speed Measurement Techniques for Bea...

引用

UNIfied conference of International Workshop on Defence Applications of Multi-Agent systems, DAMAS 2023, International conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and performance Engineering network, TEPEN 2023

作者： Ren, Zhijun Lin, Tantao Zhu, Yongsheng Hong, Jun Feng, Ke Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong University Xi’an710049 China School of Engineering The University of British Columbia KelownaV1V1V7 Canada

ISBN: (纸本)9783031494208

Rotational speed measurement is a fundamental technology used in various industries to monitor and control the speed of rotating equipment. Accurate speed measurement is crucial for optimizing performance, ensuring safety, and monitoring equipment health. This summary explores different techniques used for measuring rotational speed, which are categorized into analog, digital, and hybrid analog-digital methods. Analog methods convert rotation into proportional signals in other media, while digital methods convert rotation into a series of electrical pulses with the number of pulses proportional to the speed. Hybrid analog-digital methods convert rotation into proportional signals and calculate the frequency of the signal to obtain speed. This summary provides guidance for the design, optimization, and application of rotational speed measurement solutions in the industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Speed regulators

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Privacy-preserved Collaborative Federated Learning Platform for Industrial internet of Things

Privacy-preserved Collaborative Federated Learning Platform ...

引用

IEEE Latin-American conference on Communications (LATINCOM)

作者： Pathiraja, Lakshan Lakshan, Isuru Kushani, Kavini Sandeepa, Chamara Gamage, Tharindu Weerasinghe, Thilina Liyanage, Madhusanka Univ Ruhuna Fac Engn Matara Sri Lanka Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9798350326871

Federated learning (FL) is an intriguing approach to privacy-preserving collaborative learning. Decentralised FL is achieving increased favour for investigation due to the mitigation of vulnerability for a single point of failure and more controllability for end users over their models. However, many existing decentralised FL systems face limitations, such as privacy concerns, latency in aggregation, and real-world implementation challenges. To mitigate these issues, we introduce a novel FL protocol with a decentralised Peer-to-Peer (P2P) system using Differential Privacy (DP). It consists of a decentralised accuracy-based weighted averaging mechanism for both enhanced privacy and model aggregating accuracy. We implement our system in both virtual and real environments to evaluate the performance of the proposed mechanism. Moreover, we perform a comparative analysis of our proposal with both existing centralised and decentralised systems. To practically demonstrate the work, we consider a real-world use case of a recommendation system using smart carts. Experimental results show that our novel approach efficiently performs privacy-preserved aggregations over a decentralised peer network.

关键词： Federated learning Peer to Peer Communication networks Privacy Machine Learning

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performance Analysis of Mininet-based Testbed for Software Defined network with P4 Programmable Data Plane 17

Performance Analysis of Mininet-based Testbed for Software D...

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17th International conference on Telecommunication systems, Services, and Applications, TSSA 2023

作者： Hasnaa, Larastya Devindira Mulyana, Eueung Nurkahfi, Galih Nugraha Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350309164

network technology has continued to evolve because of the expanding use of the internet in numerous fields. One approach for addressing the difficulties of maintaining larger networks is Software-Defined networking (SDN), which separates the control plane and data plane. Typically, SDN utilizes OpenFlow as an API for communication between the control plane and data plane. However, OpenFlow has limitations, such as the restricted set of protocols that can be defined in the header as it also does not control the switch behavior of processing the packets. SDN with programmable data plane using P4 offers flexibility in defining protocols or how packets are processed in the data plane, addressing these limitations. This Mininet-based testbed development for P4 is addressed to analyze performance through parameters such as delay, jitter, throughput, packet loss, and CPU usage. Based on the experiment, the amount of processing packets must go through has an impact on the delay seen in the testing. The jitter resulted in relatively small values which indicated stable delays. The throughput achieved in all scenarios was still far below the allocated bandwidth. This can be attributed to the allocation of bandwidth exceeding the server's specification limits where Mininet is running, leading to high CPU usage and suboptimal packet delivery. Consequently, a significant packet loss rate is observed as well. © 2023 IEEE.

关键词： Testbeds

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