Artificial intelligence (AI) continues to evolve, and one of the most significant advances in recent years is the concept of Federated Learning (FL). FL enables machine learning models to be trained collaboratively across devices or servers without centralizing data, thus ensuring privacy and efficiency.
However, the expanding field of federated learning (FL) has brought forward a unique set of challenges, especially in cross-device applications.
Challenges of Cross-Device Federated Learning
As organizations and industries seek to implement federated learning for decentralized AI, the complexities of implementing FL at scale become apparent. Two key challenges has to be addressed to fully harness the potential of FL for edge computing:
- Firstly, scalability and system heterogeneity pose significant issues. Real-world deployment of FL must account for network quality variations, device resource constraints, and clients that delay updates to the global model.
- Security, the second problem domain, is heightened in cross-device settings. With potentially millions of devices, each a potential vulnerability point, ensuring the integrity of both input data and the machine learning model is paramount. The decentralized architecture of FL, although beneficial for privacy, complicates the mitigation of threats like data poisoning and adversarial attacks on the model.
These challenges are not insurmountable but call for advanced R&D efforts. By tackling these challenges, Scaleout aims to advance the state of FL technology, making it robust and practical for a wide array of industry applications, from vehicular networks to smart city infrastructure.
Scalability and Security in Federated Learning
Our vision is to push forward federated learning (FL) for cross-device use-cases, and develop a robust system development toolkit (SDK) tailored for the rigorous demands of edge computing applications. This SDK will support scalable and secure decentralized AI in sectors like automotive, smart cities and for mobile applications.
The different goals of the SDK:
- Development of a Scalable FL System: To tackle the challenges of scalability, the project proposes creating a cutting-edge simulation testbed. This testbed will be capable of emulating over a million devices, replete with realistic network disturbances and diverse client resource constraints. By incorporating automation and flexibility in testing, this will let us further develop the SDK to cater to the intricate needs of edge AI applications, ensuring that models remain efficient and effective at scale.
- Enhancing Security in a Decentralized Environment: For security, the project sets out to understand and mitigate the vulnerabilities inherent in cross-device FL. Through rigorous research and development, the project aims to establish protocols and strategies that maintain model integrity in the face of adversarial threats. This includes exploring how client sampling and model aggregation can be fortified to defend against data poisoning and other forms of attack Here Scaleout will collaborate with leading cybersecurity researchers at Uppsala University.
The FL SDK, will be validated through a collaboration with Volvo Cars for battery energy management systems. However, the scope of the SDK is much broader. The tools and methodologies developed will be applicable across a spectrum of industries and will be integral to demonstrating the practical viability of FL in edge computing scenarios.
The Potential of an SDK for Edge Federated Learning
The project is strategically focused on transitioning Federated Learning (FL) technology from laboratory validation to functional operational prototypes. This shift is in direct response to rising digitalization trends such as edge data processing, adherence to stringent privacy regulations, and a strong focus on cybersecurity. By advancing FL to a stage where it is market-ready, the initiative is set to align with Europe's overarching digital trends and position Sweden as a leader in the digital transformation arena.
To achieve this, the project has set forth concrete, actionable objectives that are specifically designed to bridge the current divide between the theoretical potential of FL and its practical application:
- Develop a robust simulation testbed for realistic scalability testing.
- Address the 'straggler' issue to maintain model integrity and efficiency at scale.
- Enhance security against adversarial threats.
- Conduct a market analysis, ensuring the SDK meets the nuanced needs of potential users.
In sum, the project's potential stretches beyond the immediate development of technology; it paves the way for a paradigm shift in how industries approach and implement AI at the edge.
Connection to Swedish Industry Digitalization
The project undertaken by Scaleout Systems is not just a technological pursuit; it's intrinsically connected to the digital transformation of the Swedish industry. As businesses and economies embrace the fourth industrial revolution, characterized by a fusion of technologies, software and people, this project seeks to advance Sweden’s strategic capability in the following areas:
- Edge Computing: The surge in edge-generated data necessitates local data processing, with predictions indicating that over the coming years, most data will be produced outside of traditional data centers.
- Privacy and Regulation: Europe's strict data protection laws favor technologies like federated learning, which uphold privacy, ensuring compliance and customer trust.
- Cybersecurity: With critical systems facing cyber threats, this project's focus on secure, decentralized AI is essential.
With this initiative, we hope to give Swedish industries an innovative lead by enhancing data privacy, bolstering cybersecurity, and streamlining edge data processing. It's a potential next step for industry-wide innovation, positioning Sweden as a pioneer in ethical and advanced AI.