A Benchmark for Federated Hetero-Task Learning (2206.03436v3)

Abstract: 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

• The paper proposes a federated hetero-task learning framework that addresses diverse client tasks and data distributions.
• The paper introduces B-FHTL, a benchmark comprising varied datasets, protocols, and evaluation metrics for heterogeneous FL settings.
• The paper’s experimental analysis shows that personalized FL methods outperform traditional approaches in managing client-specific challenges.

A Benchmark for Federated Hetero-Task Learning: A Comprehensive Overview

The paper "A Benchmark for Federated Hetero-Task Learning" introduces a nuanced exploration of federated learning by addressing heterogeneity in both data distribution and learning tasks. This work extends the traditional federated learning paradigm to encompass scenarios that reflect the diverse and often inconsistent learning objectives encountered in practice.

Overview

This research shifts the focus from classical federated learning towards a "federated hetero-task learning" framework. The authors identify a significant gap in existing federated learning methodologies, which primarily assume a homogeneous task distribution among clients. By highlighting heterogeneity in task types and data distributions, this paper aims to increase the applicability of federated learning to a wider array of real-world scenarios.

Key Contributions

1. Federated Hetero-Task Learning Framework: The authors propose a generalized framework for federated learning, aptly named federated hetero-task learning, which considers discrepancies in task objectives across different clients. This new paradigm is more aligned with real-world applications where diverse learning goals exist.
2. B-FHTL Benchmark: The paper introduces B-FHTL, a benchmark specifically designed to simulate federated hetero-task learning scenarios. B-FHTL comprises datasets, protocols, and evaluation mechanisms accommodating varying degrees of heterogeneity. It is open-sourced for further academic exploration and practical application.
3. Dataset Diversity:
   • Graph-DC: A dataset simulating diverse classification tasks across clients.
   • Graph-DT: Incorporates both classification and regression tasks, representing heterogeneous task types.
   • Text-DT: A text dataset integrating tasks such as sentiment classification and sentence similarity prediction, further illustrating task variety.
4. FL Protocols and Evaluation: The benchmark includes protocols that comply with privacy-preserving requirements and supports comprehensive evaluation metrics suitable for federated hetero-task learning. Evaluation encompasses both aggregated metrics and client-specific performance indicators.

Experimental Analysis

The authors conduct experiments on the B-FHTL benchmark, considering methodologies from federated multi-task learning, personalized federated learning, and federated meta-learning. These experiments identify challenges and potential benefits associated with federated hetero-task learning.

• Performance Insights: The paper reveals that personalized federated learning algorithms like FedBN and meta-learning approaches like FedMAML generally outperform standard federated learning methods in heterogeneous settings. These methods leverage client-specific tuning to cater to diverse learning objectives more effectively.
• Client-Specific Results: The paper explores per-client performance, showing that clients with larger datasets tend to benefit more from federated learning, suggesting an inherent bias toward clients that contribute more data. This raises important questions about fairness in federated learning.

Implications and Future Directions

The benchmark proposed by the authors, B-FHTL, sets a foundational platform for further research into federated hetero-task learning. By broadening the scope of federated applications and encouraging interdisciplinary approaches, this work invites exploration into personalized learning, model pre-training, and AutoML within a federated context.

Future research directions could focus on enhancing fairness in heterogeneous federated learning settings, expanding datasets across different domains, and integrating novel privacy-preserving techniques. Additionally, the development of more sophisticated evaluation metrics that capture the nuances of client heterogeneity will be crucial.

In conclusion, this research provides a comprehensive benchmark that not only simulates real-world challenges in federated learning but also paves the way for future advancements in this evolving field.