A Benchmark for Federated Hetero-Task Learning
This work addresses the challenge of heterogeneity in federated learning for researchers and practitioners, though it is incremental as it builds on existing federated learning concepts by adding task heterogeneity.
The authors tackled the problem of heterogeneity in federated learning by introducing federated hetero-task learning, which accounts for differences in both data distribution and learning tasks across participants, and they developed B-FHTL, a benchmark with simulation datasets, protocols, and evaluation mechanisms to facilitate research in this area.
To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks. We also present B-FHTL, a federated hetero-task learning benchmark consisting of simulation dataset, FL protocols and a unified evaluation mechanism. B-FHTL dataset contains three well-designed federated learning tasks with increasing heterogeneity. Each task simulates the clients with different non-IID data and learning tasks. To ensure fair comparison among different FL algorithms, B-FHTL builds in a full suite of FL protocols by providing high-level APIs to avoid privacy leakage, and presets most common evaluation metrics spanning across different learning tasks, such as regression, classification, text generation and etc. Furthermore, we compare the FL algorithms in fields of federated multi-task learning, federated personalization and federated meta learning within B-FHTL, and highlight the influence of heterogeneity and difficulties of federated hetero-task learning. Our benchmark, including the federated dataset, protocols, the evaluation mechanism and the preliminary experiment, is open-sourced at https://github.com/alibaba/FederatedScope/tree/master/benchmark/B-FHTL