Communication-Efficient Learning of Deep Networks from Decentralized Data (1602.05629v4)

Abstract: Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, LLMs can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning. We present a practical method for the federated learning of deep networks based on iterative model averaging, and conduct an extensive empirical evaluation, considering five different model architectures and four datasets. These experiments demonstrate the approach is robust to the unbalanced and non-IID data distributions that are a defining characteristic of this setting. Communication costs are the principal constraint, and we show a reduction in required communication rounds by 10-100x as compared to synchronized stochastic gradient descent.

• The paper presents the FedAvg algorithm, achieving up to 34.8× speedup on IID data and 2.8× on non-IID data compared to FedSGD.
• It employs local SGD and periodic model averaging to train models across decentralized devices while preserving data privacy.
• Extensive evaluations across MNIST, CIFAR-10, and language models demonstrate FedAvg’s robustness over diverse architectures and datasets.

Federated Learning: Collaborative Machine Learning via Decentralized Data

The paper "Federated Learning: Collaborative Machine Learning via Decentralized Data" presents Federated Learning (FL), an innovative approach designed to train machine learning models across a multitude of decentralized devices while addressing data privacy and communication constraints. The authors, H. Brendan McMahan et al., emphasize mitigating the challenges of training models on privacy-sensitive and large-scale data by keeping the data localized on each device and aggregating model updates in a centralized server.

Primary Contributions

The paper makes three key contributions:

1. Identifies decentralized data from mobile devices as a significant research problem.
2. Proposes FederatedAveraging (FedAvg), a practical algorithm for federated learning, which employs local stochastic gradient descent (SGD) and periodic model averaging.
3. Provides an extensive empirical evaluation of FedAvg across diverse model architectures and datasets, demonstrating its robustness to non-IID (non-independent and identically distributed) and unbalanced data.

Problem and Motivation

With the pervasive use of mobile and edge devices, enormous quantities of private data are generated continually. Traditional centralized machine learning approaches that aggregate data at a central server for training are impractical due to privacy concerns and communication costs. Federated Learning addresses these challenges by decentralizing the training process, thus significantly reducing privacy risks and communication overhead.

FederatedAveraging Algorithm

The FedAvg algorithm operates by selecting a subset of clients in each communication round. Each client performs several epochs of local training using its dataset and sends only the resultant model updates to the central server. These updates are then averaged to update the global model. This process allows for significant reductions in the number of communication rounds needed compared to centralized training.

Empirical Evaluation

The paper's empirical evaluation involves five model architectures:

• Multilayer perceptron (MNIST 2NN)
• Convolutional Neural Networks (CNN)
• Character-level LSTM LLM
• CIFAR-10 feedforward network
• Large-scale word-level LSTM

The evaluation spans datasets including MNIST, CIFAR-10, and a Shakespeare text corpus, assessing both IID and non-IID data partitions.

Numerical Results

For instance, the FedAvg algorithm achieves a significant speedup in the number of communication rounds required to reach target accuracy compared to FedSGD:

• On the MNIST dataset using a CNN architecture, FedAvg achieves a speedup of up to 34.8x for IID data and 2.8x for non-IID data.
• In large-scale LLMing tasks, FedAvg reduced the rounds required from 820 (FedSGD) to 35, a 23x improvement.

Implications and Future Directions

Practical Implications:

Federated Learning poses a paradigm shift by enabling AI applications to leverage vast decentralized datasets without compromising user privacy. This makes it particularly suitable for applications in mobile devices, IoT, and healthcare where sensitive data cannot be aggregated centrally.

Theoretical Implications:

The success of FedAvg highlights the underexplored potential of model averaging in non-convex optimization landscapes typical of deep learning. The approach is resilient to the challenges posed by non-IID data distributions, which are common in real-world scenarios.

Future Developments:

Potential future research directions include integrating differential privacy techniques to further enhance the privacy guarantees of FL. Additionally, exploring more sophisticated optimization techniques like momentum, AdaGrad, and ADAM within the FL framework could yield further efficiency gains. There's also interest in combining FL with secure multi-party computation to prevent even the central server from accessing raw updates, ensuring end-to-end encryption and privacy.

Conclusion

Federated Learning represents a robust framework for privacy-preserving, communication-efficient machine learning on decentralized data sources. The FedAvg algorithm, with its demonstrated empirical performance, stands as a cornerstone for future innovations in decentralized AI. The implications of this research span both practical applications and theoretical foundations, heralding a new era of collaborative learning across distributed networks.