Researchers from Sony AI and KAUST have introduced FedP3 to address the challenge of federated learning (FL) in scenarios where devices possess varying capabilities and data distributions, known as model heterogeneity. FL involves training a global model using data stored locally on each device, ensuring privacy. However, accommodating these differences in devices and data distributions escalates the complexity of FL implementations. The researchers aimed to resolve the issue of client-side model heterogeneity, where devices differ in memory storage, processing capabilities, and network bandwidth.
Existing federated learning methods often train a single global model shared among all clients without considering their unique characteristics. The proposed model provides a solution to heterogeneity by personalizing models for each client and employing pruning techniques to reduce model size. FedP3 (Federated Personalized and Privacy-friendly network Pruning) is a comprehensive framework tailored for FL scenarios with model heterogeneity. It integrates dual pruning strategies: global pruning, conducted server-side to reduce the model’s size, and local pruning, performed by each client to further adapt the model based on its capabilities. Furthermore, FedP3 incorporates privacy-preserving mechanisms, ensuring that sensitive client data remains protected during the FL process.
FedP3 methodology includes several key components:
Personalization: The framework allows for the creation of unique models for each client, accommodating their specific constraints, such as computational resources and network bandwidth.
Dual Pruning: By combining global and local pruning techniques, FedP3 optimizes model size and efficiency. Global pruning reduces the overall model size, while local pruning tailors the model to each client’s capabilities and data distribution.
Privacy-Preserving Mechanisms: FedP3 ensures client privacy by minimizing the data shared with the server, typically limited to model updates rather than raw data. Additionally, the paper explores a differential privacy variant, DP-FedP3, which 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. Results demonstrate that FedP3 achieves a significant communication cost reduction while maintaining comparable performance to standard FL methods. Experiments on larger-scale models, such as ResNet18, validate FedP3’s effectiveness in heterogeneous FL settings.
The paper proposes FedP3 as a comprehensive solution to address the challenges of model heterogeneity in federated learning. By integrating personalized model creation, dual pruning strategies, and privacy-preserving mechanisms, FedP3 offers a versatile framework for efficient and secure FL implementations. The experimental results highlight FedP3’s effectiveness in reducing communication costs and maintaining performance across various datasets and model architectures.
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Pragati Jhunjhunwala is a consulting intern at MarktechPost. She is currently pursuing her B.Tech from the Indian Institute of Technology(IIT), Kharagpur. She is a tech enthusiast and has a keen interest in the scope of software and data science applications. She is always reading about the developments in different field of AI and ML.
