Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data
This work addresses the problem of improving collaborative learning for participants with decentralized data, but it is incremental as it builds upon existing federated learning concepts.
The paper tackles the limitations of federated learning in fairness, costs, and reproducibility by proposing a knowledge-centric paradigm called Knowledge Augmentation in Federation (KAF) to enhance local knowledge through collaboration, without providing concrete numerical results.
Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed Knowledge Augmentation in Federation (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data.