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Mass data generation occurring in the Internet- of-Things (IoT) requires processing to extract meaningful in- formation. Deep learning is commonly used to perform such processing. However, due to the sensitive nature of these data, it is important to consider data privacy. As such, federated learning (FL) has been proposed to address this issue. FL pushes training to the client devices and tasks a central server with aggregating collected model weights to update a global model. However, the transmission of these model weights can be costly, gradually. The trade-off between communicating model weights for aggregation and the loss provided by the global model remains an open problem. In this work, we cast this trade-off problem of client selection in FL as an optimization problem. We then design a Distributed Client Selection (DCS) algorithm that allows client devices to decide to participate in aggregation in hopes of minimizing overall communication cost — while maintaining low loss. We evaluate the performance of our proposed client selection algorithm against standard FL and a state-of-the-art client selection algorithm, called Power-of-Choice (PoC), using CIFAR-10, FMNIST, and MNIST datasets. Our experimental results confirm that our DCS algorithm is able to closely match the loss provided by the standard FL and PoC, while on average reducing the overall communication cost by nearly 32.67% and 44.71% in comparison to standard FL and PoC, respectively.
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Demystifying Local & Global Fairness Trade-offs in Federated Learning Using Partial Information Decomposition
This work presents an information-theoretic perspective to group fairness trade-offs in federated learning (FL) with respect to sensitive attributes, such as gender, race, etc. Existing works often focus on either global fairness (overall disparity of the model across all clients) or local fairness (disparity of the model at each client), without always considering their trade-offs. There is a lack of understanding regarding the interplay between global and local fairness in FL, particularly under data heterogeneity, and if and when one implies the other. To address this gap, we leverage a body of work in information theory called partial information decomposition (PID), which first identifies three sources of unfairness in FL, namely, Unique Disparity, Redundant Disparity, and Masked Disparity. We demonstrate how these three disparities contribute to global and local fairness using canonical examples. This decomposition helps us derive fundamental limits on the trade-off between global and local fairness, highlighting where they agree or disagree. We introduce the Accuracy and Global-Local Fairness Optimality Problem (AGLFOP), a convex optimization that defines the theoretical limits of accuracy and fairness trade-offs, identifying the best possible performance any FL strategy can attain given a dataset and client distribution. We also present experimental results on synthetic datasets and the ADULT dataset to support our theoretical findings.
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- Award ID(s):
- 2340006
- PAR ID:
- 10560032
- Publisher / Repository:
- International Conference on Learning Representations (ICLR)
- Date Published:
- ISBN:
- 9781713898658
- Format(s):
- Medium: X
- Location:
- Vienna, Austria
- Sponsoring Org:
- National Science Foundation
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