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Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge learning

IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2022年第5期9卷 3258-3271页

作者： Albaseer, Abdullatif Abdallah, Mohamed Al-Fuqaha, Ala Erbad, Aiman Hamad Bin Khlifa Univ Coll Sci & Engn Div Informat & Comp Technol Doha 5825 Qatar

In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leading to a decrease in their lifetime. This work proposes novel solutions for energy-efficient FEEL by jointly considering local training data, available computation, and communications resources, and deadline constraints of FEEL rounds to reduce energy consumption. This paper considers a system model where the edge server is equipped with multiple antennas employing beamforming techniques to communicate with the local users through orthogonal channels. Specifically, we consider a problem that aims to find the optimal user's resources, including the fine-grained selection of relevant training samples, bandwidth, transmission power, beamforming weights, and processing speed with the goal of minimizing the total energy consumption given a deadline constraint on the communication rounds of FEEL. Then, we devise tractable solutions by first proposing a novel fine-grained training algorithm that excludes less relevant training samples and effectively chooses only the samples that improve the model's performance. After that, we derive closed-form solutions, followed by a Golden-Section-based iterative algorithm to find the optimal computation and communication resources that minimize energy consumption. Experiments using MNIST and CIFAR-10 datasets demonstrate that our proposed algorithms considerably outperform the state-of-the-art solutions as energy consumption decreases by 79% for MNIST and 73% for CIFAR-10 datasets.

关键词： Servers Energy consumption Training Data models Computational modeling Resource management Performance evaluation Federated Edge learning (FEEL) Edge Intelligence Data selection learning algorithm Energy consumption Convergence rate Resource allocation

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Distributed Channel Access for Device-to-Device Communications: A Hypergraph-Based learning Solution

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IEEE COMMUNICATIONS LETTERS 2017年第1期21卷 180-183页

作者： Sun, Youming Wu, Qihui Xu, Yuhua Zhang, Yuli Sun, Fenggang Wang, Jinlong Natl Digital Switching Syst Engn & Technol Res Ct Zhengzhou 450000 Peoples R China PLA Univ Sci & Technol Coll Commun Engn Nanjing 210000 Jiangsu Peoples R China

In this letter, we propose a learning solution for distributed channel access in device-to-device communications based on a hypergraph interference model. We first define a new interference metric for a hypergraph model, and then formulate this distributed channel access problem as a local altruistic game, which is proved to be an exact potential game admitting at least one pure strategy Nash equilibrium (PNE). A distributed learning algorithm is designed to quickly achieve the optimal PNE, which can minimize the defined networks' interference metric. Simulation results show that the proposed algorithm outperforms the existing schemes and significantly improves the spectrum efficiency.

关键词： Distributed channel access device-to-device communications potential game learning algorithm hypergraph

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Distributed learning for Energy-Efficient Resource Management in Self-Organizing Heterogeneous Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2017年第10期66卷 9287-9303页

作者： Arani, Atefeh Hajijamali Mehbodniya, Abolfazl Omidi, Mohammad Javad Adachi, Fumiyuki Saad, Walid Guvenc, Ismail Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran Tohoku Univ Res Org Elect Commun Sendai Miyagi 9808577 Japan Virginia Tech Wireless VT Bradley Dept Elect & Comp Engn Blacksburg VA 24061 USA North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

In heterogeneous networks, a dense deployment of base stations (BSs) leads to increased total energy consumption, and, consequently, increased cochannel interference (CCI). In this paper, to deal with this problem, self-organizing mechanisms are proposed, for joint channel and power allocation procedures, which are performed in a fully distributed manner. A dynamic channel allocation mechanism is proposed, in which the problem is modeled as a noncooperative game, and a no-regret learning algorithm is applied for solving the game. In order to improve the accuracy and reduce the effect of shadowing, we propose another channel allocation algorithm executed at each user equipment (UE). In this algorithm, each UE reports the channel with minimum CCI to its associated BS. Then, the BS selects its channel based on these received reports. To combat the energy consumption problem, BSs choose their transmission power by employing an ON-OFF switching scheme. Simulation results show that the proposed mechanism, which is based on the second proposed channel allocation algorithm and combined with the ON-OFF switching scheme, balances load among BSs. Furthermore, it yields significant performance gains up to about 40.3%, 44.8%, and 70.6% in terms of average energy consumption, UE's rate, and BS's load, respectively, compared to a benchmark based on an interference-aware dynamic channel allocation algorithm.

关键词： Co-channel interference energy efficiency heterogeneous networks learning algorithm

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Optimal Attack Strategies Against Predictors - learning From Expert Advice

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2018年第1期13卷 6-19页

作者： Anh Truong Etesami, S. Rasoul Etesami, Jalal Kiyavash, Negar Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL 61801 USA Princeton Univ Dept Elect Engn Princeton NJ 08644 USA Univ Illinois Dept Elect & Comp Engn Coordinated Sci Lab Urbana IL 61801 USA Univ Illinois Ind & Enterprise Syst Engn Urbana IL 61801 USA

Motivated by many real-world examples, such as recommendation systems or sensor fusion, and aiming to capture the influence of malicious experts who intentionally degrade the performance of learning systems, we analyze optimal adversarial strategies against the weighted average prediction algorithm in the learning with expert advice framework. All but one expert is honest and the malicious expert's goal is to sabotage the performance of the algorithm by strategically providing dishonest recommendations. We formulate the problem as a Markov decision process and analyze it under various settings. For the logarithmic loss, somewhat surprisingly, we prove that the optimal strategy for the adversary is the greedy policy, i.e., lying at every step. For the absolute loss, in the 2-experts, discounted cost setting, we prove that the optimal strategy is a threshold policy, where the malicious expert tells the truth until he earns enough weight and then lies afterwards. We extend the results to the infinite horizon problem and find the exact thresholds for the stationary optimal policy. Finally, we use a mean field approach in the N-experts setting to find the optimal strategy when the predictions of the honest experts are independent and identically distributed. We justify our results using simulations throughout this paper.

关键词： learning algorithm expert advice threshold policy malicious experts dynamic programming Markov decision process weighted average prediction

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Novel deep reinforcement learning-based delay-constrained buffer-aided relay selection in cognitive cooperative networks

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ELECTRONICS LETTERS 2020年第21期56卷 1148-1150页

作者： Huang, Chong Zhong, Jie Gong, Yu Abdullah, Zaid Chen, Gaojie Univ Leicester Sch Engn Leicester Leics England Zhejiang Univ Sch Engn Hangzhou Zhejiang Peoples R China Loughborough Univ Sch Engn Loughborough Leics England

In this Letter, a deep reinforcement learning-based approach is proposed for the delay-constrained buffer-aided relay selection in a cooperative cognitive network. The proposed learning algorithm can efficiently solve the complicated relay selection problem, and achieves the optimal throughput when the buffer size and number of relays are large. In particular, the authors use the deep-Q-learning to design an agent to estimate a specific action for each state of the system, which is then utilised to provide an optimum trade-off between throughput and a given delay constraint. Simulation results are provided to demonstrate the advantages of the proposed scheme over conventional selection methods. More specifically, compared to the max-ratio selection criteria, where the relay with the highest signal-to-interference ratio is selected, the proposed scheme achieves a significant throughput gain with higher throughput-delay balance.

关键词： decode and forward communication relay networks (telecommunication) cooperative communication cognitive radio learning (artificial intelligence) telecommunication computing novel deep reinforcement learning-based delay-constrained buffer-aided relay selection cognitive cooperative networks deep reinforcement learning-based approach cognitive network learning algorithm complicated relay selection problem buffer size deep-Q-learning delay constraint

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learning of associative memory networks based upon cone-like domains of attraction

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NEURAL NETWORKS 1997年第9期10卷 1649-1658页

作者： Niijima, K Kyushu Univ Dept Informat Kasuga Fukuoka 816 Japan

A learning algorithm for single layer perceptrons is proposed First, a cone-like domain is derived such that all its elements can be recognized as a stored pattern in the perceptron network. The learning algorithm is obtained as a process that enlarges the cone-like domain. For autoassociative networks, it is shown that the cone-like domain becomes a domain of attraction for a stored pattern in the network. In this case, extended domains of attraction are also obtained by feeding the outputs of the network back to the input layer In computer simulations, character recognition ability of the autoassociative network is examined. (C) 1997 Elsevier Science Ltd. All rights reserved.

关键词： cone-like domains learning algorithm autoassociative memory networks extended domains of attraction pattern recognition

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A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 82卷 1-10页

作者： Hassan, Saima Khosravi, Abbas Jaafar, Jafreezal Khanesar, Mojtaba Ahmadieh Univ Teknol PETRONAS Dept Comp & Informat Sci Tronoh 31750 Perak Malaysia Deakin Univ Ctr Intelligent Syst Res Waurn Ponds Campus Geelong Vic 3217 Australia Semnan Univ Fac Elect & Comp Engn Semnan Iran Kohat Univ Sci & Technol Inst Informat Technol Kohat Kpk Pakistan

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Extreme learning machine Interval type-2 fuzzy logic systems Electricity load forecasting learning algorithm Smart grid

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PAC-learning in the presence of one-sided classification noise

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ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2014年第4期71卷 283-300页

作者： Simon, Hans Ulrich Ruhr Univ Bochum Fak Math D-44780 Bochum Germany

We derive an upper and a lower bound on the sample size needed for PAC-learning a concept class in the presence of one-sided classification noise. The upper bound is achieved by the strategy "Minimum One-sided Disagreement". It matches the lower bound (which holds for any learning strategy) up to a logarithmic factor. Although "Minimum One-sided Disagreement" often leads to NP-hard combinatorial problems, we show that it can be implemented quite efficiently for some simple concept classes like, for example, unions of intervals, axis-parallel rectangles, and TREE(2,n,2,k) which is a broad subclass of 2-level decision trees. For the first class, there is an easy algorithm with time bound O(m logm). For the second-one (resp. the third-one), we design an algorithm that applies the well-known UNION-FIND data structure and has an almost quadratic time bound (resp. time bound O(n (2) m logm)).

关键词： Classification noise learning algorithm Computational complexity Data structures

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Distributed learning-Based Resource Allocation for Self-Organizing C-V2X Communication in Cellular Networks

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 1719-1736页

作者： Banitalebi, Najmeh Azmi, Paeiz Mokari, Nader Arani, Atefeh Hajijamali Yanikomeroglu, Halim Tarbiat Modares Univ Dept Elect & Comp Engn Tehran 14115 Iran Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Carleton Univ Dept Syst & Comp Engn Ottawa ON K1S 5B6 Canada

In this paper, we investigate a resource allocation problem for a Cellular Vehicle to Everything (C-V2X) network to improve energy efficiency of the system. To address this problem, self-organizing mechanisms are proposed for joint and disjoint subcarrier and power allocation procedures which are performed in a fully distributed manner. A multi-agent Q-learning algorithm is proposed for the joint power and subcarrier allocation. In addition, for the sake of simplicity, it is decoupled into two sub-problems: a subcarrier allocation sub-problem and a power allocation sub-problem. First, to allocate the subcarrier among users, a distributed Q-learning method is proposed. Then, given the optimal subcarriers, a dynamic power allocation mechanism is proposed where the problem is modeled as a non-cooperative game. To solve the problem, a no-regret learning algorithm is utilized. To evaluate the performance of the proposed approaches, other learning mechanisms are used which are presented in Fig. 8. Simulation results show the multi-agent joint Q-learning algorithm yields significant performance gains of up to about 11% and 18% in terms of energy efficiency compared to proposed disjoint mechanism and the third disjoint Q-learning mechanism for allocating the power and subcarrier to each user;however, the multi-agent joint Q-learning algorithm uses more memory than disjoint methods.

关键词： Resource management Device-to-device communication Q-learning Games Interference learning systems Uplink Cellular vehicle-to-everything (C-V2X) communication PD-NOMA resource allocation learning algorithm

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Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine learning Model

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APPLIED CLINICAL INFORMATICS 2022年第2期13卷 431-438页

作者： Morse, Keith E. Brown, Conner Fleming, Scott Todd, Irene Powell, Austin Russell, Alton Scheinker, David Sutherland, Scott M. Lu, Jonathan Watkins, Brendan Shah, Nigam H. Pageler, Natalie M. Palma, Jonathan P. Stanford Univ Dept Pediat Sch Med Div Pediat Hosp Med Stanford CA 94305 USA Lucile Packard Childrens Hosp Informat Serv Dept Palo Alto CA USA Stanford Univ Dept Biomed Data Sci Palo Alto CA 94304 USA Harvard Med Sch Boston MA 02115 USA Stanford Univ Dept Pediat Div Nephrol Stanford CA 94305 USA Stanford Univ Dept Pediat Sch Med Div Pediat Crit Care Med Stanford CA 94305 USA Stanford Univ Dept Pediat Div Syst Med Sch Med Stanford CA 94305 USA Orlando Hlth Div Neonatol Dept Pediat Orlando FL USA

Objective The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital. Methods The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs);(2) performance of a "membership model";and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes. Results The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data ( p = 0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables ( p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC = 0.71, p <0.0001) and the response distributions were significantly different ( p <0.0001) for the two settings. Conclusion This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.

关键词： learning algorithm electronic health record monitoring machine learning safety

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