Federated Learning (FL) has gained lots of traction recently, both in industry and academia. In FL, a machine learning model is trained using data from various end-users arranged in committees across several rounds. Since such data can often be sensitive, a primary challenge in FL is providing privacy while still retaining utility of the model. Differential Privacy (DP) has become the main measure of privacy in the FL setting. DP comes in two flavors: central and local. In the former, a centralized server is trusted to receive the users' raw gradients from a training step, and then perturb their aggregation with some noise before releasing the next version of the model. In the latter (more private) setting, noise is applied on users' local devices, and only the aggregation of users' noisy gradients is revealed even to the server. Great strides have been made in increasing the privacy-utility trade-off in the central DP setting, by utilizing the so-called matrix mechanism. However, progress has been mostly stalled in the local DP setting. In this work, we introduce the distributed matrix mechanism to achieve the best-of-both-worlds; local DP and also better privacy-utility trade-off from the matrix mechanism. We accomplish this by proposing a cryptographic protocol that securely transfers sensitive values across rounds, which makes use of packed secret sharing. This protocol accommodates the dynamic participation of users per training round required by FL, including those that may drop out from the computation. We provide experiments which show that our mechanism indeed significantly improves the privacy-utility trade-off of FL models compared to previous local DP mechanisms, with little added overhead.

Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is proprietary, confidential, and distributed, FL has become a promising approach to leverage such data effectively, particularly in sensitive domains such as medicine and the electric grid. Heterogeneity and security are the key challenges in FL, however; most existing FL frameworks either fail to address these challenges adequately or lack the flexibility to incorporate new solutions. To this end, we present the recent advances in developing APPFL, an extensible framework and benchmarking suite for federated learning, which offers comprehensive solutions for heterogeneity and security concerns, as well as user-friendly interfaces for integrating new algorithms or adapting to new applications. We demonstrate the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization. We further highlight the extensibility of APPFL through case studies in vertical, hierarchical, and decentralized FL. APPFL is open-sourced at https://github.com/APPFL/APPFL.

The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated learning (FL) has been proposed. This paper addresses the performance challenges of short-term load forecasting models trained with FL on heterogeneous data, emphasizing privacy preservation through model obfuscation. Our proposed algorithm, Privacy Preserving Federated Learning (PPFL), incorporates personalization layers for localized training at each smart meter. Additionally, we employ a differentially private mechanism to safeguard against data leakage from shared layers. Simulations on the NREL ComStock dataset corroborate the effectiveness of our approach.

Personalization aims to characterize individual preferences and is widely applied across many fields. However, conventional personalized methods operate in a centralized manner and potentially expose the raw data when pooling individual information. In this paper, with privacy considerations, we develop a flexible and interpretable personalized framework within the paradigm of Federated Learning, called PPFL (Population Personalized Federated Learning). By leveraging canonical models to capture fundamental characteristics among the heterogeneous population and employing membership vectors to reveal clients' preferences, it models the heterogeneity as clients' varying preferences for these characteristics and provides substantial insights into client characteristics, which is lacking in existing Personalized Federated Learning (PFL) methods. Furthermore, we explore the relationship between our method and three main branches of PFL methods: multi-task PFL, clustered FL, and decoupling PFL, and demonstrate the advantages of PPFL. To solve PPFL (a non-convex constrained optimization problem), we propose a novel random block coordinate descent algorithm and present the convergence property. We conduct experiments on both pathological and practical datasets, and the results validate the effectiveness of PPFL.