Researchers at Sony AI and KAUST introduced FedP3 to address federated learning (FL) challenges in scenarios where devices have different capabilities and data distributions, known as model heterogeneity. FL uses data stored locally on each device to train a global model, ensuring privacy. However, accommodating these differences in devices and data distribution increases the complexity of FL implementation. The researchers aimed to solve the problem of heterogeneity in client-side models, where devices differ in memory storage, processing power, and network bandwidth.
Existing federated learning methods often train a single global model that is shared across all clients without considering client-specific characteristics. The proposed model provides a solution to heterogeneity by personalizing the model for each client and employing pruning techniques to reduce the model size. FedP3 (Federated Personalized and Privacy-Friendly Network Pruning) is a comprehensive framework tailored for FL scenarios with a heterogeneous mix of models. It integrates his two pruning strategies: global pruning performed on the server side to reduce the size of the model, and local pruning performed by each client to further adapt the model based on features. Masu. Additionally, FedP3 has built-in privacy protection mechanisms to ensure that sensitive client data is protected during the FL process.
The FedP3 methodology includes several key components.
- Personalization: This framework enables the creation of unique models for each client that accommodate client-specific constraints such as computational resources and network bandwidth.
- Dual pruning: FedP3 optimizes model size and efficiency by combining global and local pruning techniques. Global pruning reduces the overall model size, while local pruning adjusts the model to each client's capabilities and data distribution.
- Privacy protection mechanism: FedP3 ensures client privacy by minimizing the data shared with the server, typically limited to model updates rather than raw data. Additionally, this paper also considers DP-FedP3, a variant of differential privacy that introduces controlled noise to further protect client privacy.
The performance of FedP3 is evaluated through extensive experimental studies using benchmark datasets such as CIFAR10/100, EMNIST-L, and FashionMNIST. The results show that FedP3 achieves significant reductions in communication costs while maintaining performance comparable to standard FL schemes. Experiments on large-scale models such as ResNet18 verify the effectiveness of FedP3 in heterogeneous FL settings.
In this paper, we propose FedP3 as a comprehensive solution to address the challenge of model heterogeneity in federated learning. FedP3 provides a versatile framework for efficient and secure FL implementation by integrating personalized model creation, double pruning strategies, and privacy protection mechanisms. Experimental results highlight the effectiveness of FedP3 in reducing communication costs and maintaining performance across different datasets and model architectures.
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Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her bachelor's degree at Indian Institute of Technology (IIT), Kharagpur. She is a technology enthusiast and has a keen interest in software and data and a range of science applications. She is constantly reading about developments in various areas of AI and ML.
