Federated Learning with Reduced Information Leakage and Computational Cost

Keskeiset käsitteet

Upcycled-FL, a novel federated learning strategy that applies first-order approximation at every even round of model update, can significantly reduce information leakage and computational cost while maintaining model performance.

Tiivistelmä

The paper proposes a novel federated learning strategy called Upcycled-FL that reduces information leakage and computational cost compared to standard federated learning algorithms.

Key highlights:

1. Upcycled-FL applies first-order approximation at every even round of model update, allowing the central server to update the global model without accessing the clients' local data. This reduces the total information leakage by half.
2. The even-round updates in Upcycled-FL only involve addition/subtraction operations on existing model parameters, significantly reducing the computational and transmission costs.
3. Theoretical analysis shows that Upcycled-FL converges under certain conditions, and the total privacy loss can be bounded more tightly compared to standard federated learning algorithms.
4. Experiments on both synthetic and real-world datasets demonstrate that Upcycled-FL can be adapted to various federated learning algorithms and consistently improves the privacy-accuracy trade-off.

Tilastot

"The total privacy loss experienced by each agent accumulates over iterations and privacy loss only comes from odd iterations. In contrast, if consider differentially private FedProx, accumulated privacy loss would come from all iterations."
"To achieve the same privacy guarantee, private Upcycled-FL requires much less perturbation per iteration than private FedProx. As a result, accuracy can be improved significantly."

Lainaukset

"Upcycled-FL, a simple yet effective strategy that applies first-order approximation at every even round of model update. Under this strategy, half of the FL updates incur no information leakage and require much less computational and transmission costs."
"Extensive experiments on both synthetic and real-world data show that the Upcycled-FL strategy can be adapted to many existing FL frameworks and consistently improve the privacy-accuracy trade-off."

Tärkeimmät oivallukset

Federated Learning with Reduced Information Leakage and Computation

by Tongxin Yin,... klo arxiv.org 10-02-2024

https://arxiv.org/pdf/2310.06341.pdf

Syvällisempiä Kysymyksiä

How can Upcycled-FL be extended to handle more complex federated learning scenarios, such as those involving dynamic client participation or non-convex local objectives?

Upcycled-FL can be adapted to handle dynamic client participation by incorporating mechanisms that allow for the flexible selection of clients at each iteration. This can be achieved by implementing a client selection strategy that accounts for the availability and performance of clients in real-time. For instance, a weighted selection approach could prioritize clients based on their previous contributions to the model's accuracy or their computational capabilities. Additionally, to address non-convex local objectives, Upcycled-FL can integrate advanced optimization techniques such as adaptive learning rates or momentum-based updates during the odd iterations. This would allow the algorithm to better navigate the complex loss landscapes typical of non-convex functions. Furthermore, leveraging techniques like federated meta-learning could enhance the model's ability to generalize across diverse client data distributions, thereby improving performance in dynamic and heterogeneous environments.

What are the potential drawbacks or limitations of the Upcycled-FL strategy, and how can they be addressed?

One potential drawback of the Upcycled-FL strategy is its reliance on first-order approximations during even iterations, which may lead to suboptimal updates if the local loss functions exhibit significant non-linearity or if the model parameters are far from the optimal solution. To mitigate this limitation, a more sophisticated approximation method, such as second-order methods or adaptive approximations, could be employed to enhance the accuracy of the updates. Additionally, while Upcycled-FL reduces information leakage by limiting data exposure, it may still be vulnerable to certain privacy attacks, particularly if the odd iterations are not sufficiently protected. Implementing stronger differential privacy mechanisms, such as adaptive noise addition based on the sensitivity of the updates, can help bolster privacy guarantees. Lastly, the computational savings achieved by Upcycled-FL may vary depending on the specific federated learning framework used; thus, conducting thorough empirical evaluations across different scenarios is essential to ensure consistent performance improvements.

Can the principles of Upcycled-FL be applied to other distributed learning paradigms beyond federated learning to reduce information leakage and computational costs?

Yes, the principles of Upcycled-FL can be effectively applied to other distributed learning paradigms, such as distributed optimization and decentralized learning. The core idea of reusing intermediate computations to minimize information leakage and reduce computational costs is broadly applicable. For instance, in distributed optimization scenarios, where multiple agents collaborate to solve a common optimization problem, the Upcycled approach can be utilized to limit the frequency of data sharing among agents, thereby enhancing privacy while maintaining efficiency. Additionally, in decentralized learning frameworks, where data is distributed across multiple nodes without a central server, the Upcycled strategy can facilitate local updates that leverage previous computations, reducing the need for frequent communication and thus lowering the overall communication overhead. By adapting the Upcycled principles to these contexts, researchers can explore innovative ways to enhance privacy and efficiency in various distributed learning settings.