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Federated learning (FL) is a collaborative machine-learning (ML) framework particularly suited for ML models requiring numerous training samples, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Random Forest, in the context of various applications, e.g., next-word prediction and eHealth. FL involves various clients participating in the training process by uploading their local models to an FL server in each global iteration. The server aggregates these models to update a global model. The traditional FL process may encounter bottlenecks, known as the straggler problem, where slower clients delay the overall training time. This paper introduces the Latency-awarE Semi-synchronous client Selection and mOdel aggregation for federated learNing (LESSON) method. LESSON allows clients to participate at different frequencies: faster clients contribute more frequently, therefore mitigating the straggler problem and expediting convergence. Moreover, LESSON provides a tunable trade-off between model accuracy and convergence rate by setting varying deadlines. Simulation results show that LESSON outperforms two baseline methods, namely FedAvg and FedCS, in terms of convergence speed and maintains higher model accuracy compared to FedCS.
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Towards Understanding Biased Client Selection in Federated Learning
Federated learning is a distributed optimization paradigm that enables a large number of resource-limited client nodes to cooperatively train a model without data sharing. Previous works analyzed the convergence of federated learning by accounting for data heterogeneity, communication/computation limitations, and partial client participation. However, most assume unbiased client participation, where clients are selected such that the aggregated model update is unbiased. In our work, we present the convergence analysis of federated learning with biased client selection and quantify how the bias affects convergence speed. We show that biasing client selection towards clients with higher local loss yields faster error convergence. From this insight, we propose Power-of-Choice, a communication- and computation-efficient client selection framework that flexibly spans the trade-off between convergence speed and solution bias. Extensive experiments demonstrate that Power-of-Choice can converge up to 3 times faster and give 10% higher test accuracy than the baseline random selection.
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- Award ID(s):
- 2107024
- PAR ID:
- 10356192
- Date Published:
- Journal Name:
- Proceedings of The 25th International Conference on Artificial Intelligence and Statistics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Federated learning (FL) has emerged as a new paradigm of machine learning (ML) with the goal of collaborative learning on the vast pool of private data available across distributed edge devices. The focus of most existing works in FL systems has been on addressing the challenges of computation and communication heterogeneity inherent in training with edge devices. However, the crucial impact of I/O and the role of limited on-device storage has not been explored fully in FL context. Without policies to exploit the on-device storage for placement of client data samples, and schedule clients based on I/O benefits, FL training can lead to inefficiencies, such as increased training time and impacted accuracy convergence. In this paper, we propose FedCaSe, a framework for efficiently caching client samples in-situ on limited on-device storage and scheduling client participation. FedCaSe boosts the I/O performance by exploiting a unique characteristic--- the experience, i.e., relative impact on overall performance, of data samples and clients. FedCaSe utilizes this information in adaptive caching policies for sample placement inside the limited memory of edge clients. The framework also exploits the experience information to orchestrate the future selection of clients. Our experiments with representative workloads and policies show that compared to the state of the art, FedCaSe improves the training time by 2.06× for accuracy convergence at the scale of thousands of clients.more » « less
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