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Cross-silo privacy-preserving federated learning (PPFL) is a powerful tool tocollaboratively train robust and generalized machine learning (ML) modelswithout sharing sensitive (e.g., healthcare of financial) local data. To easeand accelerate the adoption of PPFL, we introduce APPFLx, a ready-to-useplatform that provides privacy-preserving cross-silo federated learning as aservice. APPFLx employs Globus authentication to allow users to easily andsecurely invite trustworthy collaborators for PPFL, implements severalsynchronous and asynchronous FL algorithms, streamlines the FL experimentlaunch process, and enables tracking and visualizing the life cycle of FLexperiments, allowing domain experts and ML practitioners to easily orchestrateand evaluate cross-silo FL under one platform. APPFLx is available online athttps://appflx.link

APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Service