Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random client selection technique, poses several threats to the system's integrity due to the possibility of malicious client selection. Primarily, the existing literature focuses on assessing the trustworthiness of clients, neglecting the crucial aspect of trust in federated servers. To bridge this gap, in this work, we propose a novel framework that addresses the mutual trustworthiness in federated learning by considering the trust needs of both the client and the server. Our approach entails: (1) Creating preference functions for servers and clients, allowing them to rank each other based on trust scores, (2) Establishing a reputation-based recommendation system leveraging multiple clients to assess newly connected servers, (3) Assigning credibility scores to recommending devices for better server trustworthiness measurement, (4) Developing a trust assessment mechanism for smart devices using a statistical Interquartile Range (IQR) method, (5) Designing intelligent matching algorithms considering the preferences of both parties. Based on simulation and experimental results, our approach outperforms baseline methods by increasing trust levels, global model accuracy, and reducing non-trustworthy clients in the system.

Containerization technology plays a crucial role in Federated Learning (FL) setups, expanding the pool of potential clients and ensuring the availability of specific subsets for each learning iteration. However, doubts arise about the trustworthiness of devices deployed as clients in FL scenarios, especially when container deployment processes are involved. Addressing these challenges is important, particularly in managing potentially malicious clients capable of disrupting the learning process or compromising the entire model. In our research, we are motivated to integrate a trust element into the client selection and model deployment processes within our system architecture. This is a feature lacking in the initial client selection and deployment mechanism of the On-Demand architecture. We introduce a trust mechanism, named "Trusted-On-Demand-FL", which establishes a relationship of trust between the server and the pool of eligible clients. Utilizing Docker in our deployment strategy enables us to monitor and validate participant actions effectively, ensuring strict adherence to agreed-upon protocols while strengthening defenses against unauthorized data access or tampering. Our simulations rely on a continuous user behavior dataset, deploying an optimization model powered by a genetic algorithm to efficiently select clients for participation. By assigning trust values to individual clients and dynamically adjusting these values, combined with penalizing malicious clients through decreased trust scores, our proposed framework identifies and isolates harmful clients. This approach not only reduces disruptions to regular rounds but also minimizes instances of round dismissal, Consequently enhancing both system stability and security.

Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session authenticity is now feasible by monitoring users' behaviour while interacting with a mobile or Internet of Things (IoT) device, making credential theft and session hijacking ineffective. Such a technique is made possible by integrating the power of artificial intelligence and Machine Learning (ML). Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats. In this paper, we propose a novel Federated Learning (FL) approach that protects the anonymity of user data and maintains the security of his data. We present a warmup approach that provides a significant accuracy increase. In addition, we leverage the transfer learning technique based on feature extraction to boost the models' performance. Our extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and UMDAA-02-FD, show a significant increase in user authentication accuracy while maintaining user privacy and data security.

Brazilian startup Cobli has specialized in technological solutions for vehicle fleet monitoring and management. It is currently focusing on safety and refining a tool to identify driver behavioral patterns by analyzing data collected by a solar-powered tracker. The project is based on machine learning, an application of artificial intelligence, and had the support from the São Paulo Research Foundation--FAPESP through its Innovative Research in Small Business Program (PIPE). "The algorithm uses the data collected to establish a driving profile with more than 90% accuracy," says engineer Rodrigo Mourad, a partner and co-founder of Cobli. According to Mourad, in one or two weeks of use, the system can glean a sufficient amount of data--on speed, acceleration, braking and curve angles--to produce a profile of the driver's vehicle handling habits. Directly linked to the question of traffic safety, these data also have an economic and financial impact on the fleet owner's business since aggressive driving increases fuel consumption and the cost of vehicle maintenance.

Brazilian startup Cobli has specialized in technological solutions for vehicle fleet monitoring and management. It is currently focusing on safety and refining a tool to identify driver behavioral patterns by analyzing data collected by a solar-powered tracker. The project is based on machine learning, an application of artificial intelligence, and had the support) from the São Paulo Research Foundation - FAPESP through its Innovative Research in Small Business Program (PIPE http://www.bv.fapesp.br/en/3). "The algorithm uses the data collected to establish a driving profile with more than 90% accuracy," says engineer Rodrigo Mourad, a partner and co-founder of Cobli. According to Mourad, in one or two weeks of use, the system can glean a sufficient amount of data - on speed, acceleration, braking and curve angles - to produce a profile of the driver's vehicle handling habits.

Artificial intelligence, once relegated to the realm of science fiction, is now found in everything from translation services to virtual assistants to video games. And as companies race to develop self-driving cars and offer increasingly personalized online experiences, they're building on research that was largely pioneered by a group of Canadian researchers who are still attracting plenty of attention and investment dollars. Montreal, in particular, has developed a concentration of expertise in the area of AI, largely thanks to the efforts of University of Montreal professor Yoshua Bengio, head of the Montreal Institute for Learning Algorithms (MILA). "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg,'' Bengio told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers.''

Shibl Mourad, the head of engineering for Google's Montreal office, says the company hopes to help turn the city into a "super-cluster" of AI knowledge that will attract corporate investors, burgeoning startups and researchers. He said much of the credit goes to Bengio and his colleagues, whose research over the last decade has put the city ahead of its competitors. Had these researchers not invested that decade of their lives, "we would not be where we are," Mourad said. The lab Bengio leads is one of the largest in the world dedicated to studying Deep Learning, one of the underpinnings of AI. Over the past decade, they learned that by layering several "neural networks" that mimic how the brain works, computer programs could "learn" to solve complex problems on their own instead of needing to be programmed step-by-step.

Google is doubling down in Canada's artificial intelligence scene. The search giant said Monday that it's creating a new AI research group in its Montreal office and will invest $4.5 million over three years in the Montreal Institute for Learning Algorithms, an AI research lab part of the University of Montreal. Google's goog new Montreal AI research outpost will be part of Google's Brain team, the search giant's company wide-AI research group headquartered in Mountain View, wrote Google Montreal head of engineering Shibl Mourad in a blog post. Google hired Hugo Larochelle, who was recently a top research scientist at Twitter twtr, to lead the new Montreal unit. Part of Google's investment will involve funding renowned AI expert Yoshua Bengio's research projects as head of the Montreal Institute for Learning Algorithms.

Artificial intelligence, once relegated to the realm of science fiction, is now found in everything from translation services to virtual assistants to video games. And as companies race to develop self-driving cars and offer increasingly personalized online experiences, they're building on research that was largely pioneered by a group of Canadian researchers who are still attracting plenty of attention and investment dollars. Montreal, in particular, has developed a concentration of expertise in the area of AI, largely thanks to the efforts of Universite de Montreal professor Yoshua Bengio, head of the Montreal Institute for Learning Algorithms (MILA). "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg," Bengio told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers."

Artificial intelligence, once relegated to the realm of science fiction, is now found in everything from translation services to virtual assistants to video games. And as companies race to develop self-driving cars and offer increasingly personalized online experiences, they're building on research that was largely pioneered by a group of Canadian researchers who are still attracting plenty of attention and investment dollars. Montreal, in particular, has developed a concentration of expertise in the area of AI, largely thanks to the efforts of University of Montreal professor Yoshua Bengio, head of the Montreal Institute for Learning Algorithms (MILA). "(AI) will affect pretty much every economic sector; right now is just the tip of the iceberg,'' Bengio told The Canadian Press. "One of the things we are going to see more of is how these technologies affect how we interact with computers.''