Abstract Federated learning (FL) pours vitality into developing data-driven AI. However, there are still some challenges, such as balancing the security and efficiency in FL. Differential privacy is one of the dominant means in privacy-preserving machine learning. Local differential privacy (LDP) further realizes the confidentiality of the server by perturbing the transmitting parameters, which is naturally applicable for the decentralized FL. However, the current research exists the weaknesses of low communication efficiency and poor adaptability in complex deep learning models. In this work, we propose an efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony), which allows adaptive parameter range to reduce variance and improve model accuracy. Specifically, each client in each round adaptively selects perturbation parameters according to model training. Furthermore, only 1-bit data transmission for each dimension of the model parameters, thus significantly reducing the communication overhead. Theoretical analysis and proof have shown that Adaptive-Harmony holds the same asymptotic error bounds and convergence performance as advanced works but with minimal communication costs. An LDP-FL framework (Optimal LDP-FL) is also proposed, taking Adaptive-Harmony as the core. We also introduce a parameter shuffling in the Optimal LDP-FL, which avoids server tracking clients through the model parameters, thereby improving privacy levels without consuming the privacy budget. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.

As a popular distributed learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security and enjoying benefits in processing large-scale, high-dimensional data. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Local differential privacy (LDP) has recently worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel privacy-enhanced federated learning framework (Optimal LDP-FL) which achieves local differential privacy protection by the client self-sampling and data perturbation mechanisms. We theoretically analyze the relationship between the model accuracy and client self-sampling probability. Restrictive client self-sampling technology is proposed which eliminates the randomness of the self-sampling probability settings in existing studies and improves the utilization of the federated system. A novel, efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony) is also proposed, which allows an adaptive parameter range to reduce variance and improve model accuracy. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.

随着物联网技术的飞速发展,射频识别(Radio Frequency Identification,RFID)系统因具有非接触、快速识别等优点而成为了解决物联网问题的首选方案.RFID网络规划问题要考虑多个目标,被证明是多目标优化的问题.群体智能(Swarm In-telligence,SI)算法在解决多目标优化问题方面得到了广泛的关注.文中提出了一种改进型灰狼算法(Improved Grey Wolf Op-timizer,IGWO),利用高斯变异算子和惯性常量策略来实现RFID网络规划.通过建立优化模型,在满足标签100％覆盖率、部署更少的阅读器、避免信号干扰、消耗更少的功率4个目标的基础上,将所提算法与粒子群算法(Particle Swarm Optimization,PSO)、遗传算法(Genetic Algorithm,GA)、帝王蝶算法(Monarch Butterfly Algorithm,MMBO)进行了对比分析.实验结果表明,灰狼算法在RFID网络规划时表现更优异,在相同的实验环境下,相较于其他算法,IGWO的适应度值比GA提高了20.2％,比PSO提高了13.5％,比MMBO提高了9.66％;并且覆盖的标签数更多,可以更有效地求出最优化方案.

ith the rapid development of radio frequency identification (RFID) technology,in a variety of special environments (such as factories,warehouses,prisons,etc.),the demand for optimal deployment of RFID reader antennas has attracted extensive attention.In order to solve the problems in the deployment of RFID reader antenna,such as difficult deployment,many constraints and difficult to find the optimal solution and Pareto front,this paper proposes an optimized deployment method of RFID reader antenna based on the improved multi-objective SALP swarm algorithm (MSSA).The multi-objective optimization deployment model of RFID reader antenna is constructed in advance,and the optimization target is set.The multi-objective tympana algorithm is used to train the optimal deployment model of RFID reader antenna.The separation operator is introduced to optimize the search ability,and the non dominated solutions satisfying the conditions are searched continuously through iteration,and the Pareto solution set satisfying the conditions is constructed,which is the optimization result.The results show that the proposed algorithm has faster convergence rate than the algorithms of BA-OM,PSO and MC-BFO without the prior knowledge,coverage rate increases by 33%,28% and 20% respectively.Compared with the same type of hybrid firefly (HMOFA) algorithm for Pareto solution set,the load balancing is increased by 7.14%,the economic benefit is increased by 59.74%,and the reader interfe-rence is reduced by 34.04%.

针对在动态射频识别(Radio Frequency Identification,RFID)室内定位环境中,传统的室内定位模型会随着定位目标数量的增加而导致定位误差增大、计算复杂度上升的问题,文中提出了一种基于近端策略优化(Proximal Policy Optimization,PPO)的RFID室内定位算法.该算法将室内定位过程看作马尔可夫决策过程,首先将动作评价与随机动作相结合,然后进一步最大化动作回报值,最后选择最优坐标值.其同时引入剪切概率比,首先将动作限制在一定范围内,交替使用采样后与采样前的新旧动作,然后使用随机梯度对多个时期的动作策略进行小批量更新,并使用评价网络对动作进行评估,最后通过训练得到PPO定位模型.该算法在有效减少定位误差、提高定位效率的同时,具备更快的收敛速度,特别是在处理大量定位目标时,可大大降低计算复杂度.实验结果表明,本文提出的算法与其他的RFID室内定位算法(如Twin Delayed Deep Deterministic Policy Gradient(TD3),Deep Deterministic Policy Gradient(DDPG),Actor Critic using Kronecker-Factored Trust Region(ACK-TR))相比,定位平均误差分别下降了36.361％,30.696％,28.167％,定位稳定性分别提高了46.691％,34.926％,16.911％,计算复杂度分别降低了84.782％7,70.213％,63.158％.

针对现有的RFID室内定位算法的精度容易受到环境因素影响的问题,提出了一种基于异步优势动作评价(Asynchronous Advantage Actor critic,A3C)的RFID室内定位算法.该算法的主要步骤为:1)将RFID的信号强度RSSI值作为输入值,多个线程子动作网络并行交互采样学习,利用子评价网络评价动作值的优劣,使模型不断优化,找到最优信号强度RSSI值,并训练定位模型;子线程网络定期将网络参数异步更新到全局网络上,全局网络最后输出参考标签的具体位置,同时训练得到异步优势动作评价定位模型.2)在线定位阶段,当待测目标进入待测区域时,记录待测目标的信号强度RSSI值,将其输入异步优势动作评价定位模型中,子线程网络从全局网络中获取最新定位信息,对待测目标进行定位,最后输出目标的具体位置.实验数据表明,基于异步优势动作评价的RFID室内定位算法与传统的基于向量机(Support Vector Machines,SVM)定位、基于极限学习机(Extreme Learning Machine,ELM)定位、基于多层神经网络定位(Multi-Layer Perceptron,MLP)的RFID室内定位算法相比,定位平均误差分别下降了66.114％,50.316％,44.494％;定位稳定性分别平均提高了59.733％,53.083％,43.748％.实验结果表明,基于异步优势动作评价的RFID室内定位算法在处理大量室内定位目标时具有较好的定位性能.

随着科技发展,智能家居的不断出现,人们对家庭生活便利性需求也逐渐提高.通常人们在家中常苦于寻找失踪的钥匙、银行卡、钱包等细小物件,是否有方法能够快速定位从而找到贵重物品是掉落在哪个不起眼的小角落,那么如何让不说话的物体也能在家中"发声"成为了本篇文章探索与思考的方向.为满足市场化需求,通过电子条形码与RFID标签技术相结合,引入EPC编码系统实现多元化数据量存储,采用RFID无源标签与家居物件进行粘贴捆绑,操作简单且无需过多后续打理,在RFID阅读器部分,可以引入程序将信息传递至用户应用设备,让用户通过手机等移动设备实现物体数据显示及定位信息查询,轻松了解所需物品对应的所处位置,从而减少家庭物品遗失的困扰.文章针对智能家居物品定位的设计思路开展研究讨论,对智能家居创新提出新方案研究,主要对RFID技术定位的功能、阅读器技术及引入EPC编码系统应用进行说明.

随着室内定位技术的飞速发展,射频识别(Radio Frequency Identification,RFID)技术以其非接触、快速识别等优点成为解决问题的首选方案.针对目前室内定位算法的精度容易受到标签密度和算法效率的影响及对动态环境适应性不足的问题,文中提出了一种蝗虫群优化(Grasshopper Optimization Algorithm,GOA)和极限学习机(Extreme Learning Machine,ELM)相结合的RFID室内定位算法.该算法通过蝗虫群优化对极限学习机随机产生的输入层权值和隐含层阈值进行选择,以提升极限学习机的性能,从而在离线阶段减少学习时间;利用蝗虫群算法对极限学习机参数进行优化,有效克服环境以及信号强度值变化对定位精度的影响.通过实验研究了影响算法性能的因素,并验证了算法的有效性.与BP神经网络算法(NN-Based)和非度量多维尺度算法(NMDS-RFID)相比,所提算法的定位平均误差分别降低了22.32％和20.06％,平均执行时间分别减少了58.7％和7.55％.仿真和实验结果表明,所提算法在获得更精确的定位结果的同时降低了时间成本,并对环境变化具有较好的适应性.

为了解决无线射频识别(RFID)系统中多阅读器与标签通信的碰撞问题,文中将此问题建模为马尔可夫决策过程,并提出了一种基于Q-learning的防碰撞算法.该算法通过智能体agent不断与周围环境进行交互和学习,从而产生Q值函数,得到最佳信道分配策略;取消了HiQ算法中复杂的分层结构,简化了系统模型,引入ε贪婪策略以得到全局最优解,改进奖赏函数以得到最优状态.仿真结果表明,与HiQ算法和EHiQ算法相比,该智能算法能够自适应地为阅读器分配不同的信道来进行数据传输,从而有效降低碰撞率,提高信道利用率和吞吐率.

针对无线射频识别(RFID)系统中多标签-多阅读器环境下标签与信道资源的分配问题,提出了一种基于Whittle索引的多阅读器信道资源分配算法.在RFID多阅读器信道分配问题中建立无休止多臂赌博机(RMAB)模型,并采用Whittle索引算法进行求解.该算法依据信道前期的忙、闲状态,将信道空闲概率作为信任值赋予每个信道,并根据信道当前的信任值计算其Whittle索引值.标签选择索引值最大的信道作为可能感知接入的信道,随后根据每个时隙数据发送成功与否来动态更新信道信任值.对信道分配过程中可能出现的标签碰撞问题,采用等待一个时隙后再根据识别反馈信息重新选择接入信道的方式来解决.将文中所提算法从两个方面与典型的D iC a算法和Gentle算法进行比较:一是在阅读器数量固定的前提下,其系统吞吐量随待识别标签数量的变化情况;二是在待识别标签数量固定的前提下,其系统吞吐量随阅读器数量的变化情况.仿真结果表明,所提算法在上述两种情况下的系统吞吐量均优于DiCa算法和Gentle算法,其吞吐量在阅读器数量固定的前提下分别平均提高了150.34％ 和23.98％,在待识别标签数量固定的前提下分别平均提高了205.01％ 和43.37％.随着阅读器和待识别标签数量的增多,所提算法在系统吞吐量方面的优势更加明显.因此,采用提出的算法可以对有限的信道资源进行合理的动态分配,有效提高RFID多阅读器系统的识别效率.