APPFLx is a managed, service-oriented deployment of APPFL that provides federated learning capabilities as an accessible, hosted service. While APPFL offers a flexible open-source framework for building federated learning systems, APPFLx focuses on operationalizing federated learning for real-world users, teams, and institutions.
APPFLx lowers the barrier to adoption by abstracting infrastructure complexity while preserving the privacy, scalability, and extensibility guarantees of APPFL.
Deploying federated learning in production environments often requires expertise in:
Distributed systems
Secure networking
Identity and access management
Workflow orchestration across heterogeneous compute resources
APPFLx addresses these challenges by offering a ready-to-use federated learning service, allowing users to focus on models, data, and science, rather than platform engineering.
On-demand creation and management of federated learning experiments
No requirement for users to deploy or manage FL servers manually
Supports cross-organization and cross-infrastructure federations
Designed to run across:
Public cloud environments
HPC centers
Hybrid and multi-site infrastructures
Supports elastic resource provisioning and scalable execution
Data remains local to participating sites
Secure communication and authentication mechanisms
Designed to integrate with institutional identity and access controls
Centralized visibility into:
Federation status
Training rounds and progress
Model versions and metadata
Enables reproducibility and auditability of federated experiments