ProgFed: A Federated Learning Framework for Efficient Training by Network Growing

Основные понятия

ProgFed is a novel federated learning framework that reduces communication and computation costs by progressively training increasingly complex models, achieving comparable or superior performance to traditional methods.

Аннотация

• Bibliographic Information: Wang, H.-P., Stich, S. U., He, Y., & Fritz, M. (2022). ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training. Proceedings of the 39th International Conference on Machine Learning, PMLR 162.
• Research Objective: This paper introduces ProgFed, a new approach to federated learning that aims to reduce communication and computation costs by progressively training deep neural networks, starting from shallower architectures and gradually expanding to the full model.
• Methodology: ProgFed divides the target model into overlapping partitions and introduces lightweight local supervision heads to guide the training of these sub-models. The model capacity is progressively increased during training, inheriting learned weights from previous stages, until the full model is reached. The authors theoretically analyze the convergence rate of ProgFed and conduct extensive experiments on various datasets (CIFAR-10/100, EMNIST, BraTS) and architectures (VGG, ResNet, ConvNets, 3D-Unet) for both classification and segmentation tasks.
• Key Findings:
  • ProgFed achieves comparable or even superior performance to traditional federated learning methods while significantly reducing communication and computation costs.
  • The progressive training approach leads to up to 20% computation cost reduction and up to 63% communication cost reduction without sacrificing performance.
  • ProgFed is compatible with existing compression techniques like quantization and sparsification, further enhancing its efficiency.
  • The framework generalizes well to advanced federated optimization algorithms like FedProx and FedAdam.
• Main Conclusions: ProgFed offers a practical and effective solution for resource-constrained federated learning scenarios. By leveraging progressive learning, it reduces the training burden on edge devices without compromising model accuracy.
• Significance: This research contributes to the growing field of federated learning optimization by introducing a novel training paradigm that addresses the critical challenges of communication and computation efficiency.
• Limitations and Future Research: While the paper demonstrates the effectiveness of ProgFed, further investigation into hyperparameter tuning, particularly the number of stages and their durations, is warranted. Additionally, exploring the application of ProgFed in more diverse and challenging federated learning settings, such as those with highly heterogeneous data distributions, could be a promising direction for future research.

Статистика

ProgFed saves up to 20% computation and up to 63% communication costs for converged models.
The approach can achieve a wide range of trade-offs by combining these techniques, showing reduced communication of up to 50x at only 0.1% loss in utility.
ProgFed saves around 25% computation cost and up to 32% two-way communication costs in federated classification.
In federated segmentation, ProgFed reduces communication costs by 63% without sacrificing performance.
ProgFed allows for a communication cost reduction of around 2x in classification and 6.5x in U-net segmentation while achieving practicable performance (≥98% of the best baseline).

Цитаты

"We propose ProgFed, the first federated progressive learning framework that reduces both communication and computation costs while preserving model utility."
"Our method inherently reduces two-way communication costs and complements existing methods."
"ProgFed is compatible with classical compression, including sparsification and quantization, and various federated optimizations, such as FedAvg, FedProx, and FedAdam."

Ключевые выводы из

ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training

by Hui-Po Wang,... в arxiv.org 10-25-2024

https://arxiv.org/pdf/2110.05323.pdf

Дополнительные вопросы

How might the principles of ProgFed be applied to other distributed machine learning paradigms beyond federated learning?

ProgFed's core principles, namely progressive training and local supervision, hold significant potential for application in various distributed machine learning paradigms beyond federated learning. Here's how:

Distributed Training with Limited Resources: In scenarios like edge computing or mobile device training, where computational resources and bandwidth are often constrained, ProgFed's approach of gradually scaling up the model can be highly beneficial. By starting with smaller sub-models and progressively incorporating more complex components, the training process becomes more manageable for resource-limited devices.

Model Parallelism: Large-scale models often necessitate model parallelism, where different parts of the model are trained on separate devices. ProgFed's concept of dividing the model into stages and training them progressively aligns well with this paradigm. Each stage can be trained on a dedicated set of devices, and the outputs can be progressively combined, potentially reducing communication overhead and accelerating training.

Transfer Learning and Domain Adaptation: ProgFed's use of local supervision heads can be adapted for transfer learning and domain adaptation tasks. By pre-training the shallower layers on a source task and then progressively fine-tuning deeper layers on a target task with a new local head, the model can potentially learn more effectively from limited target data.

Decentralized Learning: In decentralized learning environments, where no central server orchestrates the training process, ProgFed's principles can be adapted to facilitate communication-efficient model aggregation. Devices can progressively share and merge their locally trained sub-models, gradually building towards a globally optimized model.
However, adapting ProgFed to these paradigms would require careful consideration of the specific challenges they present. For instance, communication protocols and synchronization mechanisms might need adjustments to accommodate the distributed nature of these settings.

Could the reliance on local supervision heads in ProgFed potentially introduce privacy vulnerabilities in certain federated learning applications?

Yes, the reliance on local supervision heads in ProgFed could potentially introduce privacy vulnerabilities in certain federated learning applications. Here's why:

Information Leakage through Gradients: Even though ProgFed, like other federated learning approaches, avoids directly sharing raw data, information about the training data can still be leaked through the shared gradients during the training process. Since local supervision heads are trained on local data, the gradients computed during their training might contain sensitive information that could be exploited by malicious actors to infer private details about the underlying dataset.

Adversarial Attacks: The presence of local supervision heads might expose additional attack surfaces for adversaries. For instance, an attacker could try to poison the training process by injecting carefully crafted malicious data into a subset of clients. This could lead to the local supervision heads learning biased representations, ultimately compromising the global model's performance or even leaking private information.
Mitigating Privacy Risks:
Several strategies can be employed to mitigate these privacy risks:

Differential Privacy: Techniques like differential privacy can be incorporated into the training process to add noise to the shared gradients, making it harder for adversaries to extract sensitive information.

Secure Aggregation: Employing secure aggregation protocols can ensure that the server only receives aggregated updates from multiple clients, making it difficult to isolate individual client contributions and reducing the risk of information leakage.

Robust Training Methods: Developing training methods that are robust to data poisoning attacks can help prevent malicious actors from manipulating the local supervision heads and compromising the global model.
Carefully evaluating the potential privacy implications and implementing appropriate safeguards is crucial when deploying ProgFed in privacy-sensitive federated learning applications.

If the future of machine learning is characterized by increasingly larger models and datasets, how might approaches like ProgFed need to evolve to address the accompanying resource demands?

As machine learning models and datasets continue to grow in scale, approaches like ProgFed will need to evolve significantly to address the escalating resource demands. Here are some potential directions for evolution:

Adaptive and Dynamic Progression: Instead of pre-defined stages, future ProgFed versions could incorporate adaptive and dynamic progression mechanisms. These mechanisms would analyze the learning dynamics, model complexity, and available resources in real-time to dynamically adjust the model's growth, optimizing resource utilization throughout the training process.

Hybrid Training Strategies: Combining ProgFed with other resource-efficient training techniques like model pruning, quantization, and sparse model training could lead to synergistic gains. For instance, progressively pruning less important connections during training could further reduce computational and communication costs without significantly sacrificing accuracy.

Decentralized and Heterogeneous ProgFed: Scaling ProgFed to massive datasets and models might necessitate embracing decentralized learning paradigms. This would involve developing distributed versions of ProgFed that can efficiently train across numerous devices with varying computational capabilities, potentially leveraging techniques like federated learning and gossip algorithms.

Hardware-Aware ProgFed: Future iterations of ProgFed should be designed with a hardware-aware perspective. This involves optimizing the training process for specific hardware architectures, such as GPUs, TPUs, or even emerging neuromorphic hardware, to maximize efficiency and leverage hardware acceleration capabilities.

Theoretical Advancements: Further theoretical analysis of ProgFed's convergence properties, especially in the context of large-scale models and distributed settings, will be crucial for guiding its evolution and ensuring its effectiveness in resource-constrained environments.
By embracing these advancements, ProgFed and similar approaches can play a vital role in enabling the training and deployment of increasingly powerful machine learning models on massive datasets, even with limited resources.