The goal of this paper is to predict the Remaining Useful Life (RUL) of turbine jet engines using a federated machine learning framework. Federated Learning enables multiple edge devices/nodes or servers to collaboratively train a shared model without sharing sensitive data, thus preserving data privacy and security. By implementing a nonlinear model, the system aims to capture complex relationships and patterns in the engine data to enhance the accuracy of RUL predictions. This approach leverages decentralized computation, allowing models to be trained locally at each device before aggregating the learned weights at a central server. By predicting the RUL of jet engines accurately, maintenance schedules can be optimized, downtime reduced, and operational efficiency improved, ultimately leading to cost savings and enhanced performance in the aviation industry. Computational results are provided by using the C-MAPSS dataset which is publicly available on the NASA website and is a valuable resource for studying and analyzing engine degradation behaviors in various operational scenarios.

Background: Centralized collection and processing of healthcare data across national borders pose significant challenges, including privacy concerns, data heterogeneity and legal barriers. To address some of these challenges, we formed an interdisciplinary consortium to develop a feder-ated health data network, comprised of six institutions across five countries, to facilitate Nordic-Baltic cooperation on secondary use of health data. The objective of this report is to offer early insights into our experiences developing this network. Methods: We used a mixed-method ap-proach, combining both experimental design and implementation science to evaluate the factors affecting the implementation of our network. Results: Technically, our experiments indicate that the network functions without significant performance degradation compared to centralized simu-lation. Conclusion: While use of interdisciplinary approaches holds a potential to solve challeng-es associated with establishing such collaborative networks, our findings turn the spotlight on the uncertain regulatory landscape playing catch up and the significant operational costs.

Machine Learning (ML) has shown significant potential in various applications; however, its adoption in privacy-critical domains has been limited due to concerns about data privacy. A promising solution to this issue is Federated Machine Learning (FedML), a model-to-data approach that prioritizes data privacy. By enabling ML algorithms to be applied directly to distributed data sources without sharing raw data, FedML offers enhanced privacy protections, making it suitable for privacy-critical environments. Despite its theoretical benefits, FedML has not seen widespread practical implementation. This study aims to explore the current state of applied FedML and identify the challenges hindering its practical adoption. Through a comprehensive systematic literature review, we assess 74 relevant papers to analyze the real-world applicability of FedML. Our analysis focuses on the characteristics and emerging trends of FedML implementations, as well as the motivational drivers and application domains. We also discuss the encountered challenges in integrating FedML into real-life settings. By shedding light on the existing landscape and potential obstacles, this research contributes to the further development and implementation of FedML in privacy-critical scenarios.

Yet, a significant portion is scattered and locked in data silos, leaving its potential untapped. Federated Machine Learning is a novel AI paradigm enabling the creation of AI models from decentralized, potentially siloed data. Hence, Federated Machine Learning could technically open data silos and therefore unlock economic potential. However, this requires collaboration between multiple parties owning data silos. Setting up collaborative business models is complex and often a reason for failure. Current literature lacks guidelines on which aspects must be considered to successfully realize collaborative AI projects. This research investigates the challenges of prevailing collaborative business models and distinct aspects of Federated Machine Learning. Through a systematic literature review, focus group, and expert interviews, we provide a systemized collection of socio-technical challenges and an extended Business Model Canvas for the initial viability assessment of collaborative AI projects.

Dynamic spectrum access systems typically require information about the spectrum occupancy and thus the presence of other users in order to make a spectrum al-location decision for a new device. Simple methods of spectrum occupancy detection are often far from reliable, hence spectrum occupancy detection algorithms supported by machine learning or artificial intelligence are often and successfully used. To protect the privacy of user data and to reduce the amount of control data, an interesting approach is to use federated machine learning. This paper compares two approaches to system design using federated machine learning: with and without a central node.

Glioblastoma is an aggressive and hard-to-treat type of brain cancer. But because it affects fewer than 10 in 100,000 people each year, it's considered to be a rare disease. Defining the boundaries of glioblastoma tumors is important for treatment. One key region represents the breakdown of the blood-brain barrier inside the tumor. Another, called the tumor core, could be relevant for surgical removal.

Business patterns of the past couple of years have affirmed the critical role that digital transformation trends play in an organization's ability to carry out its mission. Forward-thinking organizations must focus their efforts on enabling their people, and the people they serve, to thrive in the more distributed, more digitalized world we're living in. In 2022, it's on leaders to not just innovate, but also take what's worked and expand on it, so teams can continue to work more efficiently and collaborate more effectively in the face of unpredictable and rapid change. Here are the most promising, accessible and impactful technology solutions for organizations to focus on launching -- or perfecting -- in 2022. Legacy systems weren't designed to handle remote work at scale and immediacy, but your distributed workforce needs to stay connected.

Google introduced the term Federated Learning in 2016 to mark the beginning of a new machine learning approach in the ML paradigm. Federated learning resolves many shortcomings of the centralized and distributed training approaches. Without the use of federated learning, we would not have seen the highly improved on-device machine learning model like "Hey Google" in Google Assistant. To understand federated learning and its importance in today's IoT world, let me first describe the shortcomings of the existing models. The notion of machine learning started with centralized training.

In this special guest feature, Gary M. Shiffman, PhD, Co-founder and CEO, Consilient, takes a look at Federated Machine Learning, the branch of machine learning that's sure to be a revolution for FCC professionals by enabling collaboration while preserving privacy. Gary is an applied micro-economist and business executive working to combat organized violence, corruption, and coercion. Past experiences include senior positions at the Pentagon, U.S. Senate, and the Department of Homeland Security. He is the Founder and CEO of Giant Oak, Inc. and the Co-Founder and CEO of Consilient, Inc., machine learning and artificial intelligence companies building solutions to support professionals promoting national security and combating financial crime. Dr. Shiffman is the author of The Economics of Violence: How Behavioral Science Can Transform Our View of Crime, Insurgency, and Terrorism with Cambridge University Press in 2020.

From healthcare and government to the financial sector and beyond, advanced data science models and big data projects are unlocking insights that can deliver everything from novel approaches to preventing and treating disease to highly effective financial fraud detection and more. Organizations looking to embark on data collaboration initiatives must overcome obstacles such as data ownership issues, compliance requirements for a variety of regulations and more. In today's data-filled world, ensuring privacy and security is paramount, and the measures to which organizations must go to achieve this can make collaborative data science difficult. The potential consequences of sustaining any kind of privacy or security breach (noncompliance, fines, reputational damage, etc.) can cause organizations to shy away from sharing data sets that could spark the next life-saving medical treatment or momentous public service program. Luckily, organizations across many industries are recognizing just how much upside we're leaving on the table if valuable data sets remain siloed.