Federated learning (FL), a machine learning technique that preserves privacy by aggregating models from each device without exchanging personal data, has gained significant interest. This paper aims to establish an efficient and practical asynchronous FL method for decentralized systems. In asynchronous decentralized FL, the order of learning and aggregation is arbitrary. First, we explore the impact of this ordering on FL. Then, we propose to utilize the version information regarding model updates. Our strategy is to aggregate only the updated models after the previous round to improve the quality of the device’s model by avoiding the reaggregation of older models. Moreover, we also design a new practical framework for asynchronous decentralized FL by extending Flower framework. Our framework realizes effective communication ability by leveraging gRPC communication and thus can be applied to practical systems without the central server. Our evaluation shows the effectiveness of our methods that aggregate only the updated models from other devices. In addition, we show the impact of ordering on learning and aggregation according to situations.

With the spread and development of 5G technology, network configurations with MEC (Multi-Access Edge Computing) servers in the vicinity of 5G base stations are becoming more common. We have been researching and developing Giocci, a resource permeating distributed processing platform that offloads computation tasks on end devices to MEC servers and cloud servers. In this paper, we propose resource allocation methods to efficiently determine the server to which tasks are allocated in a network configuration that includes MEC servers. In order to construct the proposed methods, we first model the main functions of Giocci and define the resource allocation problem for this work. There are four proposed methods according to each objective and priority; a prioritized allocation by the average number of waiting tasks, by the communication delay, by the task response time, and by the cost of using computing resources. We initially implement these methods assuming that they are task allocation functions in Giocci. Experimental evaluations demonstrate that they could achieve appropriate resource allocation results for each objective. This research contributes to the smooth allocation of computational resources in 5G networks including MEC servers.

Advances in device technology have promoted the development of small appliances such as wearable devices, IoT equipment, and small home electronics equipment. Considering a product family of such device/equipment, each product in the family may have different hardware (micro controller unit, devices such as sensors and actuators, and different configurations). Software for such equipment tends to be compact and handles the devices directly via drivers. However, software engineers are not familiar with driver development and this causes development bottleneck. Therefore, in order to develop products efficiently, derivations of software and hardware are not enough, i.e., the systematic derivation of drivers is indispensable. In this paper, we propose a system product line development method for small appliances in which drivers are systematically derived. In this method, hardware features and software features are managed in terms of feature models, and system products are derived by specifying features of hardware and software. Based on these, drivers are systematically derived. This paper proposes 1) an extension of the UML/MARTE model that can represent hardware information that is necessary for driver generation, and 2) a systematic driver derivation method based on variability management and model-driven engineering techniques. We evaluate the method using an example of a motion tracking system product family.

Uncertainty can appear in all aspects of software development: uncertainty in requirements analysis, design decisions, implementation, and testing. As the research on uncertainty is so young, there are many issues to be tackled. Modularity for Uncertainty is one of them. If uncertainty can be dealt with modularly, we can add or delete uncertain concerns to/from models, code, and tests whenever these concerns arise or are fixed to certain concerns. To deal with this challenging issue, we propose a modularization mechanism for uncertainty. Agile methods embrace change to accept changeable user requirements. On the other hand, our approach embraces uncertainty to support exploratory development. This paper sets out a focused research agenda for uncertainty in terms of the new modularity vision.

Uncertainty in programming is one of the challenging issues to be tackled, because it is error-prone for many programmers to temporally avoid uncertain concerns only using simple language constructs such as comments and conditional statements. This paper proposes ucJava, a new Java programming environment for conquering uncertainty. Our environment provides a modular programming style for uncertainty and supports test-driven development taking uncertainty into consideration.

This paper rethinks both modularity and compilation in the light of abstraction between design and implementation. We propose a new compilation approach called abstraction-aware verifying compiler, in which abstraction is the target of compilation. Both a design model and its code are inputted as the first-class software modules to the compiler.

Abstraction has been an important issue in software engineering. However, it is not easy to design an architec- ture reflecting the intention of developers and implement the result of a design as a program while preserving an adequate abstraction level. To deal with this problem, we provide iArch, an IDE (Integrated Development Environment) for supporting abstraction-aware traceability between design and code. The iArch IDE is based on Archface, an architectural interface mechanism exposing a set of architectural points that should be shared between design and code. An abstraction level is determined by selecting architectural points.

Our society depend on embedded and ubiquitous computing and the reliability of embedded software becomes more and more important. We have conducted a five years project with industries to develop software for realizing highly reliable embedded systems. We have tackled reliability issues from the following aspects. (1)Design environment: we have developed a UML design verification tool that apply model checking techniques to improve the design quality of application software. (2)Operating environment: we have developed operating system supports that realize multiple execution of real-time operating systems and also developed operating systems with enhanced resource management. Both technologies contribute the realization of robust run-time environment. (3) Real-time environment: we have developed real-time garbage collection techniques for Jave. They prevent the suspension of applications that violates the correct behavior of real-time applications. Also, they reduce the effort of application programmers to avoid garbage-collection during important execution timing. We have obtained fruitful results from these three research themes, and some of them are actually used in industries. Furthermore, we have integrated the results to make synergetic effect of them. In order to demonstrate the effectiveness, we have conduct an experiment. In this paper, we introduce the project and its results.