首页|Researchers from Swiss Federal Institute of Technology Report Details of New Stu dies and Findings in the Area of Machine Learning (Towards Cross-silo Federated Learning for Corporate Organizations)
Researchers from Swiss Federal Institute of Technology Report Details of New Stu dies and Findings in the Area of Machine Learning (Towards Cross-silo Federated Learning for Corporate Organizations)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - Research findings on Machine Learning are discussed in a new report. According to news reporting out of Zurich, Switze rland, by NewsRx editors, research stated, “Digital media companies rely on mach ine learning models to target their content toward their audience’s interests. M achine learning models usually rely on the amount and quality of training data.” Our news journalists obtained a quote from the research from the Swiss Federal I nstitute of Technology, “While today, data is abundant, it is typically stored i n data silos and cannot be shared between companies or publishers due to data pr otection and user privacy. Federated Learning (FL) is a distributed machine lear ning approach that is rapidly gaining popularity and enables collaboratively tra ining machine learning models on a large corpus of decentralized data. Prior res earch on FL mainly focuses on an FL setup containing millions of clients. For ex ample, a client may be a single user’s mobile device with data. However, we note that, in many scenarios, corporate organizations such as news media companies t hat have available data from multiple sets of users could also benefit from FL. In this work, we aim to focus on building FL models where multiple corporate org anizations like news media companies or banks participate in the training proces s of FL to collaboratively train federated models. We used federated learning to train models for a set of corporate stakeholders and applied FL for two tasks: a classification task and a ranking task. For the classification task, we design ed a tree-based federated random forest algorithm and a neural network-based fed erated algorithm. For the ranking task, we designed a federated neural ranking m odel for news article recommendations. Our experimental results demonstrate that corporate companies by participating in FL can achieve benefits in improving th e model performance in terms of accuracy for classification tasks and in terms o f ranking for recommendation tasks.”
ZurichSwitzerlandEuropeCyborgsEm erging TechnologiesMachine LearningSwiss Federal Institute of Technology
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