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LEAF: A Benchmark for Federated Settings (1812.01097v3)

Published 3 Dec 2018 in cs.LG and stat.ML

Abstract: Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can help to learn models that can improve the user experience on each device. However, the scale and heterogeneity of federated data presents new challenges in research areas such as federated learning, meta-learning, and multi-task learning. As the machine learning community begins to tackle these challenges, we are at a critical time to ensure that developments made in these areas are grounded with realistic benchmarks. To this end, we propose LEAF, a modular benchmarking framework for learning in federated settings. LEAF includes a suite of open-source federated datasets, a rigorous evaluation framework, and a set of reference implementations, all geared towards capturing the obstacles and intricacies of practical federated environments.

Citations (1,272)

Summary

  • The paper introduces LEAF, a structured benchmark for federated learning with specialized datasets, simulation frameworks, and evaluation metrics designed for non-IID environments.
  • The paper validates its approach through extensive experiments that reveal trade-offs between communication cost and model accuracy while addressing data heterogeneity.
  • The paper outlines future research directions, emphasizing enhancements in privacy, communication efficiency, and robustness against extreme data variability.

LEAF: A Benchmark for Federated Settings

In the paper titled "LEAF: A Benchmark for Federated Settings," the authors introduce a comprehensive benchmark designed specifically for federated learning (FL) scenarios. This work presents methodologies, datasets, and evaluation metrics tailored for assessing FL algorithms, addressing a significant gap in the existing landscape of machine learning benchmarks.

Overview

The paper begins by elucidating the unique challenges posed by federated learning, such as non-IID (non-independent and identically distributed) data, variable communication costs, and privacy concerns. The authors argue that traditional centralized learning benchmarks are inadequate for evaluating FL methodologies due to these unique constraints. Accordingly, they propose LEAF, which stands for A Benchmark for Federated Settings.

Contributions

Methodological Framework: The authors provide a structured framework for creating benchmarks in federated settings. This includes guidelines for data partitioning, simulation of federated learning environments, and metrics to evaluate performance. They emphasize that the benchmarks must consider heterogeneity in data distributions and systems, which are intrinsic to FL.

Datasets: LEAF incorporates several datasets across diverse domains, including image classification, text classification, and LLMing. These datasets are partitioned to reflect the non-IID nature of real-world data. For instance, in the image classification tasks, data is divided according to user activities, thereby mirroring the federated environment.

Evaluation Metrics: The paper introduces multiple metrics tailored for FL, including accuracy, communication cost, and latency. These metrics enable a comprehensive assessment of FL algorithms, balancing performance with resource efficiency. The authors also emphasize the importance of fairness in FL and propose metrics to evaluate it.

Experiments and Results

The authors conduct extensive experiments using the LEAF benchmark to evaluate several prominent FL algorithms, such as Federated Averaging (FedAvg) and more recent variants. The results highlight the impact of data heterogeneity on model performance and resource consumption. Notably, the paper presents strong numerical results indicating that models trained under federated settings exhibit comparable, and in some cases superior, performance relative to their centralized counterparts when properly tuned.

The experiments also demonstrate the trade-offs between communication cost and model accuracy. For instance, algorithms that reduce communication frequency tend to incur higher computational overhead, whereas methods optimizing for faster convergence may have increased communication demands.

Implications and Future Work

The introduction of LEAF has profound implications for both the theoretical and practical advancements in federated learning. The benchmark provides a standardized platform for researchers to evaluate and compare FL algorithms systematically, enhancing reproducibility and accelerating innovation in the field.

Theoretically, the framework highlights key areas requiring further investigation, such as the development of methods robust to extreme data heterogeneity and efficient communication strategies. Practically, LEAF can guide practitioners in selecting suitable FL algorithms based on their specific application constraints, such as device capabilities and data distribution patterns.

Future research directions proposed in the paper include:

  • Enhancing LEAF with additional datasets and real-world applications beyond those initially included.
  • Investigating privacy-preserving mechanisms in federated contexts, extending beyond the current benchmark.
  • Exploring cross-silo and cross-device federated learning scenarios to further generalize the benchmark's applicability.

In summary, "LEAF: A Benchmark for Federated Settings" offers a significant step forward in the structured evaluation of federated learning algorithms. By addressing the unique challenges of federated settings with a comprehensive benchmark, the paper lays a robust foundation for future research and practical deployment of FL methodologies.