In particular, developing data-driven machine learning models can provide early identification of patients with high risk for diabetes, potentially leading to more effective therapeutic strategies and reduced healthcare costs. However, regulation restrictions create barriers to developing centralized predictive models. This paper addresses the challenges by introducing a federated learning approach, which amalgamates predictive models without centralized data storage and processing, thus avoiding privacy issues. This marks the first application of federated learning to predict diabetes using real clinical datasets in Canada extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) without crossprovince patient data sharing. We address class-imbalance issues through downsampling techniques and compare federated learning performance against province-based and centralized models. Experimental results show that the federated MLP model presents a similar or higher performance compared to the model trained with the centralized approach. However, the federated logistic regression model showed inferior performance compared to its centralized peer. Introduction Predicting diabetes based on patient risk factors is paramount for the Canadian and global populations due to its significant impact on public health and healthcare costs. The number of patients with chronic disease, including diabetes, in Ontario, Canada alone, increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased by 12.2% to 6.5 million

Kelly Fisher started using a robo-advisor a year and a half ago because she thought it would be more convenient and easier than investing through a human advisor. What she didn't anticipate, though, was just how much more truthful she would be with an automaton rather than a living, breathing person sitting across the desk. When someone starts asking me about my net worth, I get uncomfortable. The San Francisco-based retail executive has about 8,000 invested in accounts with robo-advisors. These are sites that ask a series of questions and then they match a fund with that investor's risk tolerance and lifestyle.

The study found that 55% of Canadian adults would like their employer to provide extended leave opportunities, 45% would prefer not to work at fixed times (i.e. "Flexibility and empowerment will be the new work currencies and productivity will be redefined," says Badminton. "Flexible payment schedules for workers will come into effect administered by automated systems that measure output, not hours put in." Finally, many people are also concerned that work is interfering with their personal lives. "Whether it's cutting corners to save time, or paying other people to do their job for them, Canadian adults are considering some unique ways to take back control at work" says Black.