1st Workshop on Green Federated Learning: Toward Sustainable AI for Decentralized Future

AIM & SCOPE

The Green Federated Learning (GFL) workshop aims to address the urgent need for sustainability in AI, specifically in the context of Federated Learning (FL). While FL offers significant benefits such as enhanced privacy and decentralized training, its scalability and energy consumption pose growing challenges. As machine learning and AI models become larger and more complex, their environmental footprint—through both energy consumption and carbon emissions—becomes increasingly significant.

The primary goal of this workshop is to explore and promote methods for reducing the environmental impact of Federated Learning while ensuring the continued advancement of AI technologies. We seek to bring together researchers, engineers, policymakers, and sustainability experts to:

1. Identify the environmental challenges faced by Federated Learning systems, including energy inefficiencies and carbon emissions.

2. Explore and develop novel strategies for optimizing Federated Learning algorithms, communication protocols, and infrastructure to reduce energy consumption and environmental impact.

3. Foster interdisciplinary collaboration between AI practitioners, green computing researchers, and sustainability experts to develop sustainable Federated Learning systems that balance privacy, performance, and energy efficiency.

4. Promote the adoption of green AI practices in real-world decentralized applications, from edge computing to IoT and mobile devices, ensuring AI remains a force for good in both technological and environmental contexts.

5. Create a roadmap for sustainable AI practices that can be integrated into Federated Learning models, ensuring their widespread adoption and long-term viability in an environmentally-conscious future.

The scope of the workshop covers a wide range of topics, from energy-efficient algorithms and sustainable infrastructure to policy and regulatory frameworks that can guide the development of Green Federated Learning solutions. By providing a platform for collaboration and knowledge exchange, the workshop will contribute to the creation of greener, more sustainable AI systems in a decentralized world.