FedSOL: Stabilized Orthogonal Learning in Federated Learning
Core Concepts
Federated Stabilized Orthogonal Learning (FedSOL) proposes an orthogonal learning strategy to balance conflicting objectives in Federated Learning, aiming to preserve global knowledge while effectively training on local datasets.
Abstract
FedSOL introduces a novel method to address data heterogeneity in Federated Learning by balancing local and proximal objectives. By identifying gradients orthogonal to the proximal objective, FedSOL achieves state-of-the-art performance across various scenarios. The approach of FedSOL involves perturbing weight parameters adversarially and updating them based on the local gradient at these perturbed weights. This method ensures that the resulting local gradient remains orthogonal to the proximal gradient, maintaining global knowledge during local learning. The adaptive perturbation strength and partial perturbation strategies enhance FedSOL's performance and efficiency. Extensive experiments validate the efficacy of FedSOL in preserving global knowledge and improving the smoothness of the global model.
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FedSOL
Stats
FedSOL consistently achieves state-of-the-art performance across various scenarios.
Perturbing weight parameters using proximal objectives helps maintain performance on the global distribution.
Adaptive perturbation strength reflects global and local parameter discrepancies.
Partial perturbation focusing on specific layers reduces computational requirements.
Quotes
"FedSOL aims to find a parameter region where the local gradient is stable against proximal perturbations."
"Our experiments demonstrate that FedSOL consistently achieves state-of-the-art performance across various scenarios."
Deeper Inquiries
How does FedSOL compare to other federated learning algorithms in terms of scalability
FedSOL offers scalability benefits compared to other federated learning algorithms due to its ability to address data heterogeneity issues effectively. By incorporating an orthogonal learning strategy, FedSOL can balance conflicting objectives during local learning without compromising performance. This approach allows for better preservation of global knowledge across diverse client datasets, leading to improved model convergence and generalization. Additionally, FedSOL's adaptive perturbation strength and partial perturbation techniques contribute to its scalability by reducing computational requirements while maintaining high performance levels.
What are potential drawbacks or limitations of using an orthogonal learning strategy like FedSOL
While FedSOL provides significant advantages in federated learning, there are potential drawbacks and limitations associated with using an orthogonal learning strategy like FedSOL. One limitation is the complexity of implementing orthogonal gradients in a distributed setting where clients have overlapping data distributions. This complexity can lead to increased computational demands and communication overhead, especially when dealing with large-scale datasets or numerous clients. Additionally, the need for careful parameter tuning in FedSOL may pose challenges in real-world applications where resources are limited or time-sensitive.
How can concepts from continual learning be further integrated into federated learning for enhanced performance
Integrating concepts from continual learning into federated learning can further enhance performance by addressing key challenges such as catastrophic forgetting and model adaptation over time. By leveraging strategies like memory retention for past tasks and gradient projection onto orthogonal spaces, federated learning algorithms can improve knowledge retention across multiple rounds of training without interference between local and global objectives. Continual learning techniques also enable models to adapt more efficiently to new data distributions while preserving previously learned information, ultimately enhancing the overall robustness and efficiency of federated learning systems.
