Workshop on Federated Learning Systems

Dimitris Stripelis · Chaoyang He · Hongyi Wang · Tian Li · Praneeth Vepakomma · Bo Li · Eric Xing

Room 242

We organize this workshop to spur further research in the intersection of federated learning algorithmic optimization, model and data privacy & security, and federated learning systems efficiency and scalability.

Topics of interest include, but are not limited to:
• Challenges of FL systems deployment.
• Design and development of scalable FL systems.
• FL systems automation.
• FL systems in real-world, practical and production settings.
• FL systems with federated data management awareness.
• FL systems tailored for different learning applications, such as medical, finance, and manufacturing.
• FL systems tailored for different learning topologies, such as centralized, decentralized, and hierarchical.
• FL systems tailored for different data partitioning schemes, such as horizontal, vertical, and hybrid.
• FL systems with self-tuning capabilities.
• FL systems with failover capabilities.
• FL systems benchmark and evaluation.
• Data value and economics of data federations and FL systems.
• Auditable FL systems.
• Explainable FL systems.
• Interpretable FL systems.
• FL systems open challenges and vision perspectives.
• Incentives for formatting large-scale federations across organizations.
• Operational challenges in FL systems.
• Resilient and robust FL systems.
• Standardization of FL systems.
• Trade-offs between FL systems privacy, security, and efficiency.
• Trustworthy FL systems.
• Privacy, security, and hardware co-designs for FL systems.

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Timezone: America/Los_Angeles