Federated Learning Taxonomy: Beyond Weights and Gradients
A new study released on arXiv (2606.16891) offers a precise mathematical framework for federated messages, addressing gaps in existing definitions that overlook modern payloads like synthetic data and federated analytics. The researchers introduce a system that classifies these messages into three types: model structures, statistical summaries, and data-conditioned representations. They evaluate these categories based on their computational needs, communication costs, and privacy implications, shedding light on the trade-offs involved in decentralized training. An examination of 202 recent papers reveals a significant shift since 2021 towards diverse messaging strategies, moving away from traditional deep learning updates to more customized information sharing. This framework aims to guide future developments in the evolving area of federated learning, which is advancing beyond just model weights and gradients.
Key facts
- Paper arXiv:2606.16891 proposes a formal mathematical definition of a federated message.
- Taxonomy categorizes federated messages into model structures, statistical summaries, and data-conditioned representations.
- Evaluation criteria include computational demands, communication costs, and privacy risks.
- Review of 202 recent publications shows a shift since 2021 toward diverse messaging paradigms.
- The paper addresses the gap in existing definitions for modern payloads like synthetic data and federated analytics.
- The framework aims to clarify trade-offs in decentralized training.
- The paper is available on arXiv with abstract ID 2606.16891.
- The announcement type is replace-cross.
Entities
Institutions
- arXiv