Query-based Knowledge Transfer for Heterogeneous Learning Environments

However, existing solutions like federated learning, ensembles, and transfer learning, often fail to adequately serve the unique needs of clients, especially when local data representation is limited. To address this issue, we propose a novel framework called Query-based Knowledge Transfer (QKT) that enables tailored knowledge acquisition to fulfill specific client needs without direct data exchange. QKT employs a data-free masking strategy to facilitate communication-efficient query-focused knowledge transfer while refining task-specific parameters to mitigate knowledge interference and forgetting. Our experiments, conducted on both standard and clinical benchmarks, show that QKT significantly outperforms existing collaborative learning methods by an average of 20.91% points in single-class query settings and an average of 14.32% points in multi-class query scenarios. Further analysis and ablation studies reveal that QKT effectively balances the learning of new and existing knowledge, showing strong potential for its application in decentralized learning. However, the rapid proliferation of Internet of Things (IoT) devices and the increasingly stringent data privacy regulations have highlighted the need for a decentralized machine learning framework. This framework allows models to be trained locally on devices or within organizations and encourages knowledge transfer between models in the network of clients without exchanging raw data. Despite its potential, the decentralized paradigm faces substantial challenges, particularly in addressing the diverse needs of devices and clients in heterogeneous environments. In heterogeneous environments, each client may have vastly different local data distributions, resulting in diverse query objectives that might be out of the local distribution but relevant to other clients. For instance, in medical diagnostics, models may be required to detect rare or emerging diseases that are underrepresented locally, necessitating the ability to generalize from similar conditions observed in other regions or populations. Similarly, in fraud detection, the constantly evolving nature of fraudulent activities means that new tactics may not yet be captured in the historical data of certain clients. Consequently, it is helpful for models to rapidly learn from fraud patterns detected elsewhere to remain effective. Previous work has offered valuable solutions to this challenge, but each comes with its own limitations. Collaborative methods like Federated Learning (FL) (McMahan et al., 2017) aggregate knowledge across clients but often struggle to adapt models to the specific needs of individual clients.