As the fourth fundamental circuit element, the memristor may execute computations while storing data. Fracmemristor takes advantage of the fractional calculate’s long-term memory, non-locality, weak singularity, and the memristor’s storage–computational integration. Since the physical structure of the fracmemristor is similar to the topology of the ant transfer probability flow in ACO, we propose the fractional-order memristive ant colony algorithm (FMAC), which uses the fracmemristor physical system to record the probabilistic transfer information of the nodes that the ant will crawl through in the future and pass it to the current node of the ant, so that the ant acquires the ability to predict the future transfer. After instigating the optimization capabilities with TSP, we discovered that FMAC is superior to PACO-3opt, the best integer-order ant colony algorithm currently available. FMAC operates substantially more quickly than the fractional-order memristor ant colony algorithm due to the transfer probability prediction module based on the physical fracmemristor system (FACA). 

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A Study of Fractional-Order Memristive Ant Colony Algorithm: Take Fracmemristor into Swarm Intelligent Algorithm


 Discrete memristor has become a hotspot since it was proposed recently. However, the design of chaotic maps based on discrete memristor is in its early research stage. In this paper, a memristive seed chaotic map is proposed by combining a quadratic discrete memristor with the Sine function. Furthermore, by applying the chaotification method, we obtain a high-dimensional chaotic map. Numerical analysis shows that it can generate hyperchaos. With the increase of cascade times, the generated map has more positive Lyapunov exponents and larger hyperchaotic range. The National Institute of Standards and Technology (NIST) test results show that the chaotic pseudo-random sequence generated by cascading two seed maps has good unpredictability, and it indicates the potential in practical application.

A novel hyperchaotic map with Sine chaotification and discrete memristor

Discrete memristor has become a hotspot since it was proposed recently. However, the design of chaotic maps based on discrete memristor is in its early research stage. In this paper, a memristive seed chaotic map is proposed by combining a quadratic discrete memristor with the sine function. Furthermore, by applying the chaotification method, we obtain a high-dimensional chaotic map. Numerical analysis shows that it can generate hyperchaos. With the increase of cascade times, the generated map has more positive Lyapunov exponents and larger hyperchaotic range. The National Institute of Standards and Technology (NIST) test results show that the chaotic pseudo-random sequence generated by cascading two seed maps has good unpredictability, and it indicates the potential in practical application.

A novel hyperchaotic map with sine chaotification and discrete memristor


 The memristor is also a basic electronic component, just like resistors, capacitors, and inductors. It is a nonlinear device with memory characteristics. In 2008, with HP’s announcement of the discovery of the TiO2 memristor, the new memristor system, memory capacitor(memcapacitor) and memory inductor(meminductor) were derived. Fractional-order calculus has the characteristics of non locality, weak singularity and long term memory which traditional integer-order calculus does not have, and can accurately portray or model real-world problems better than the classic integer-order calculus.In recent years, researchers have extended the modeling method of memristor by fractional calculus, and proposed the fractional-order memristor, but its concept is not unified. This paper reviews the existing memristive elements, including integer-order memristor systems and fractional-order memristor systems. We analyze their similarities and differences, give the derivation process, circuit schematic diagrams, and an outlook on the development direction of fractional-order memristive elements.

A Mathematical analysis: from Memristor to Fracmemristor

Federated learning refers to conducting training on multiple distributed devices and collecting model weights from them to derive a shared machine-learning model. This allows the model to get benefit from a rich source of data available from multiple sites. Also, since only model weights are collected from distributed devices, the privacy of those data is protected. It is useful in a situation where collaborative training of machine learning models is necessary while training data are highly sensitive. This study aims at investigating the implementation of lightweight federated learning to be deployed on a diverse range of distributed resources, including resource-constrained edge devices and resourceful cloud servers. As a resource management framework, the FogBus2 framework, which is a containerized distributed resource management framework, is selected as the base framework for the implementation. This research provides an architecture and lightweight implementation of federated learning in the FogBus2. Moreover, a worker selection technique is proposed and implemented. The worker selection algorithm selects an appropriate set of workers to participate in the training to achieve higher training time efficiency. Besides, this research integrates synchronous and asynchronous models of federated learning alongside with heuristic-based worker selection algorithm. It is proven that asynchronous federated learning is more time efficient compared to synchronous federated learning or sequential machine learning training. The performance evaluation shows the efficiency of the federated learning mechanism implemented and integrated with the FogBus2 framework. The worker selection strategy obtains 33.9% less time to reach 80% accuracy compared to sequential training, while asynchronous further improve synchronous federated learning training time by 63.3%.

Wu-Yang Zhu

Papers

A Mathematical analysis: from Memristor to Fracmemristor

Federated Learning Framework in Fogbus2-based Edge Computing Environments