The Heterogeneity and Privacy in Cross-Silo Federated Learning
Abstract
In addressing the twin challenges of data heterogeneity and privacy in Federated Learning (FL), this thesis introduces a dual-faceted approach that not only pioneers advancements in FL methodologies but also contributes significantly to the discourse on privacy preserving techniques within the domain. Our research is divided into two primary foci: improving FL performance amid data heterogeneity through a novel Federated Learning with Partially Personalized (FedPP) method and optimizing privacy-preserving mechanisms to balance efficacy and confidentiality in FL implementations. The first segment of our investigation unveils the FedPP method, an innovative approach designed to mitigate the limitations imposed by the heterogeneity of the data in FL. By integrating the Learning Rate Adjustment (LoRA) technique, FedPP not only surpasses the performance benchmarks of centralized training models, but also sets a new standard for personalized federated learning. This methodological advancement underscores the potential of partial personalization in bridging the performance gap often witnessed in conventional FL settings, thus enhancing model accuracy and efficiency across diverse data distributions. At the same time, our exploration extends into the realm of differential privacy (DP), where we posit the existence of an optimal interplay between the number of local updates and communication rounds, a crucial balance to maximize convergence performance within a predefined privacy…
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