RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates

Cloud service capabilities can always be clearly indicated by Quality of Service (QoS) properties. The quality and effectiveness of cloud services can be measured at both client and server sides. The parameters such as response time, throughput and failure rate influence the QoS properties at the client side. These parameters may differ with the utilization of the application for the same service. The proposed Bayesian Personalized Ranking-based Rank Prediction Scheme (BPR-RPS) provides rank prediction of QoS properties on the client side, since client-side properties vary with the usage of the application. The proposed rank prediction framework provides ranking to the services when particular cloud services are requested by the client. Personalization of cloud providers and customers is made possible by concentrating on the item recommendation imposed on the cloud by creating user-specific ranking for a set of items. The past response of the user with respect to the item aids in decision-making. The proposed ranking framework includes the following modules: Computation of similarity index between the active and ad hoc users and finding of identical users based on similarity index values.

One of the challenges faced by machine learning in human activity recognition systems is the different distributions of the training and test samples. Transfer learning constitutes a solution to this problem. On the other hand, to perform transfer learning, it is necessary to have access to the original dataset. However, access to the dataset to implement the transfer learning algorithms results in a privacy breach. To deal with this challenge, this paper presents semi-supervised and unsupervised scenarios for privacy-preserving transfer learning in centralized and distributed manner. In the proposed distributed algorithms, it is not necessary to share the original data among the clients to implement the transfer learning algorithms. Instead, the transfer learning process can be fulfilled without having the original datasets. PPSETR and PPUSTR algorithms transfer the knowledge while preserving the privacy of the datasets on the client side. In contrast, PPDSETR and PPDUSTR algorithms provide the privacy protection of the distributed data on both the client and server sides. The proposed semi-supervised algorithms reduce the recognition error rate by 20.58% and the unsupervised algorithms decrease the recognition error rate by about 15.97% while these algorithms considerably preserve the privacy.

At IHEP (Institute of High Energy Physics, Chinese Academy of Sciences), computing resources are contributed by different experiments including BES, JUNO, DYW, HXMT, etc. The resources were divided into different partitions to satisfy the dedicated experiment data processing requirements. IHEP had a local Torqu&Maui cluster with 50 queues serving for above 10 experiments. The separated resource partitions leaded to imbalance resource load. In a typical situation, BES resource partition was quite busy without free slot but still with lots of jobs in idle, while JUNO resources are free and wasted seriously. After moving resources from Torque&Maui to HTCondor in 2016, job scheduling efficiency has been improved a lot. In order to balance the imbalance resource load, we designed an efficient sharing strategy to improve the overall resourceutilization. We created an unified pool shared by all experiments. For each experiment, resources are divided into two parts: dedicated resource and sharing resource. The slots in dedicated resource only run jobs from its own experiment, and the slots in sharing resource are shared by jobs from all experiments. Default ratio of dedicated resource to sharing resource is 1:4. To maximize the sharing effectiveness, the ratio is dynamically adjusted between 0:5 and 4:1 based on the number of jobs submitted by each experiment. We have developed a central control system to decide how many resources can be allocated to each experiment group. This system is implemented at two sides: server side and client side. A management database is built at server side, which is storing resource, group and experiment information. Once the sharing ratio needs to be adjusted, resource group will be changed and updated into database. The resource group information is published to the server buffer in real-time. The client periodically pulls resource group information from server buffer via https protocol And resource scheduling configuration at client side is changed based on the resource group information. With this method, share ratio can be modified and deployed dynamically. Sharing strategy is implemented with HTCondor. ClassAd mechanism and accounting-group in HTCondor facilitate to utilizethe sharing strategy at IHEP computing cluster. With the sharing strategy, resource usage has been improved dramatically.

In recent years, the type and quantity of news are growing rapidly, and it is not easy for users to find the news they are interested in the massive amount of news. A news recommendation system can score and predict the candidate news, and finally recommend the news with high scores to users. However, existing user models usually only consider users’ long-term interests and ignore users’ recent interests, which affects users’ usage experience. Therefore, this paper introduces gated recurrent unit (GRU) sequence network to capture users’ short-term interests and combines users’ short-term interests and long-term interests to characterize users. While existing models often only use the user’s browsing history and ignore the variability of different users’ interest in the same news, we introduce additional user’s ID information and apply the personalized attention mechanism for user representation. Thus, we achieve a more accurate user representation. We also consider the risk of compromising user privacy if the user model training is placed on the server side. To solve this problem, we design the training of the user model locally on the client side by introducing a federated learning framework to keep the user’s browsing history on the client side. We further employ secure multi-party computation to request news representations from the server side, which protects privacy to some extent. Extensive experiments on a real-world news dataset show that our proposed news recommendation model has a better improvement in several performance evaluation metrics. Compared with the current state-of-the-art federated news recommendation models, our model has increased by 0.54% in AUC, 1.97% in MRR, 2.59% in nDCG@5%, and 1.89% in nDCG@10. At the same time, because we use a federated learning framework, compared with other centralized news recommendation methods, we achieve privacy protection for users.

The widespread use of Internet of Things (IoT) technology has promoted location‐based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k‐anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client‐server‐to‐user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G‐V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k‐anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.

A lot of service requires identity of users to mitigate undesirable incidents such as fraud. To cut down probability of potential fraud, ID Card of users are collected to be verified so people can verify users whenever an undesirable activity happens. However, to verify identity through an ID card, a repository for the ID Card is required. To verify ID cards, the ID Card repository will be connected to an automated ID Card Verification API. The ID Card repository is meant to be used both on mobile phones and desktop computers, so the concept of progressive web application is used. To be able to upload images smoothly and build progressive web application, the ID Card repository is built using ReactJS and Ant Design. Server side is powered by Strapi and MongoDB. GraphQL API is utilized to connect client side and server side. It involves queries to fetch data. To fetch data on the client side, Apollo client is used to in ReactJS. Git is utilized for version control system which gives contribution to Continuous Integration and Continuous Delivery. In this paper, we will discuss why Strapi is best suited on server side and how Ant Design, a library to style components in a web page, can provide required components in this web application.

We describe a distributed system for scientific applications based on sharable extendible arrays. In our sharing scheme, a part of or all of an extendible array placed on the server side can be shared among many clients. A sharing array can be declared local to a client, and it initially shares an arbitrary part of an extendible array on the server side. After the initial sharing, it can be extended locally on the client. The locally extended part on the client side together with the shared part on the server side can be logically handled as a single extendible array. A set of extendible arrays can be persistently stored on secondary storage. In this paper, after proposing the properties of extendible arrays and their manipulation, we propose the notion of sharing an extendible array. Then the language constructs and the runtime routines for managing extendible arrays and sharing arrays are described stressing their scope and visibility in the client/server system architecture.KeywordsServer SideClient SideExtendible ArrayHistory TableLock ModeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Web applications are one of the most useful platforms for the delivery of the information and service over the internet today. Day-to-Day the popularity of web application usage is increasing. Hence the web apps has to be designed for secure transformation of information from web client to web server and to mitigate the vulnerabilities. This paper presents a research survey report on constructing a secure web applications at the client side, server side and even in browsers. This paper initially gives an overview of web application, then followed by techniques or tools along with the attacks at the client side and serverside. It gives a big picture with the aim of promoting future research in this area.

ABSTRACTIn practical, real‐world complex networks, data distribution is frequently decentralized and Non‐Independently Identically Distributed (Non‐IID). This heterogeneous data presents a significant challenge for federated learning. Such problems include the generation of biased global models, the lack of sufficient personalization capability of local models, and the difficulty in absorbing global knowledge. We propose a Federated Learning Approach Based on Client Clustering and Generator‐based Knowledge Distillation(CGKDFL) for heterogeneous data. Firstly, to reduce the global model bias, we propose a clustering federated learning approach that only requires each client to transmit some of the parameters of the selected layer, thus reducing the number of parameters. Subsequently, to circumvent the absence of global knowledge resulting from clustering, a generator designed to improve privacy features and increase diversity is developed on the server side. This generator produces feature representation data that aligns with the specific tasks of the client by utilizing the labeling information provided by the client. This is achieved without the need for any external dataset. The generator then transfers its global knowledge to the local model. The client can then utilize this information for knowledge distillation. Finally, extensive experiments were conducted on three heterogeneous datasets. The results demonstrate that CGKDFL outperforms the baseline method by a minimum of , , and regarding accuracy on the three heterogeneous datasets. Additionally, it outperforms the compared methods regarding convergence speed in all cases.

A Linux operating system release is composed of a large number of software packages, with complex dependencies. The management of dependency relationship is the foundation of building and maintaining a Linux operating system release, and checking the integrity of the dependencies is the key of the dependency management. The widespread adoption of Linux operating systems in many areas of the information technology society has drawn the attention on the issues regarding how to check the integrity of complexity dependencies of Linux packages and how to manage a huge number of packages in a consistent and effective way. Linux distributions have already provided the tools for managing the tasks of installing, removing and upgrading the packages they were made of. A number of tools have been provided to handle these tasks on the client side. However, there is a lack of tools that could help the distribution editors to maintain the integrity of Linux package dependencies on the server side. In this paper we present a method based on conflict to check the integrity of Linux package dependencies. From the perspective of conflict, this method achieves the goal to check the integrity of package dependencies on the server side by removing the conflict associating with the packages. Our contribution provides an effective and automatic way to support distribution editors in handling those issues. Experiments using this method are very successful in checking the integrity of package dependencies in Linux software distributions.

This paper presents an original caching method called Minimal Quadtree Replication (MQTR) for Web and Mobile Applications (WMA) to query and display large sets of objects on a map. This method pre-builds a quadtree at the server side that is incrementally replicated at the client side. Our method tends to avoid useless queries and to minimize the overlaps of content when similar queries to remote services are done. This caching method offers benefits that increase the quality of service (QoS) provided by the WMA using it, when measured in terms of CPU time, bandwidth, and response time. We compared our MQTR approach with common approaches that are currently used by some popular web sites. Results show that MQTR significantly outperforms the other approaches. The best variant of our method reduces in average the number of requests by a factor of 5.5, transfers 3.9 times less data and requires 2.4 times less CPU time at the server side.

In this paper we demonstrate our MPEG-DASH evaluation suite, which comprises several components on the client side as well as on the server side. The major client components are the VLC DASH plugin, libDASH, and DASH-JS, a JavaScript-based DASH client. These tools enable performance tests on various platforms, e.g., Windows and Linux as well as mobile platforms such as Android. Moreover, due to their flexible structure it is possible to integrate adaptation logics and evaluate them under consistent conditions. On the server side we provide the content generation tool DASHEncoder, our MPEG-DASH datasets well as the MPEG-DASH conformance validator.

E-Yuan multimedia system is developed for the rich audio and video resource on the Internet and on its server side, it can automatically search and integration of network video and audio resources, and send to the client side for the user in real-time broadcast TV viewing, full use of remote control operation, Simply it’s a very easy to use multimedia system. This article introduces its infrastructure, main technical ideas and you can also see some details about server side and client side. At the same time, the improvement on how to collect and integrate video resources is comprehensively elaborated.

E-Yuan multimedia system is developed for the rich audio and video resource on the Internet and on its server side, it can automatically search and integration of network video and audio resources, and send to the client side for the user in real-time broadcast TV viewing, full use of remote control operation, Simply it’s a very easy to use multimedia system. This article introduces its infrastructure, main technical ideas and you can also see some details about server side and client side. At the same time, the improvement on how to collect and integrate video resources is comprehensively elaborated.KeywordsLinuxEmbedded systemWeb 2.0ServerClientVideo search

Context: Most approaches to automated white-box testing consider the client side and the server side of a web application in isolation from each other. Such testers lack a whole-program perspective on the web application under test. Inquiry: We hypothesise that an additional whole-program perspective would enable the tester to discover which server side errors can be triggered by an actual end user accessing the application through the client, and which ones can only be triggered in hypothetical scenarios. Approach: In this paper, we explore the idea of employing such a whole-program perspective in testing. To this end, we develop , a novel concolic tester which operates on full-stack JavaScript web applications, where both the client and the server side are JavaScript processes communicating via asynchronous messages -- as enabled by the WebSocket or Socket.IO-libraries. Knowledge: We find that the whole-program perspective enables discerning high-priority errors, which are reachable from a particular client, from low-priority errors, which are not accessible through the tested client. Another benefit of the perspective is that it allows the automated tester to construct practical, step-by-step scenarios for triggering server side errors from the end user's perspective. Grounding: We apply on a collection of web applications to evaluate how effective testing is in distinguishing between high- and low-priority errors. The results show that correctly classifies the majority of server errors. Importance: This paper demonstrates the feasibility of testing as a novel approach for automatically testing web applications. Classifying errors as being of high or low importance aids developers in prioritising bugs that might be encountered by users, and postponing the diagnosis of bugs that are less easily reached.