APPFL: Advanced Privacy-Preserving Federated Learning

New Features Introduce support for streamed aggregation in gRPC communication, enabling memory-efficient federated learning for large models and datasets. [Documentation]. Add a new example demonstrating federated learning with Graph Neural Networks (GNNs) using PyTorch Geometric from @trucndt. [Documentation]. Integrate additional privacy-preserving mechanisms from @aash-mohammad, including: secure aggregation and differential privacy via Octopus-Privacy. Add tutorial notebooks for running APPFL on NERSC systems, available here. Add tutorial notebooks for running APPFL on AI-READI datasets, available here. Bug Fixes Resolve race conditions in the FedCompass implementation. Fix issues when running FedCompass with Globus Compute. Improve GitHub workflows and CI/CD actions for testing and deployment stability.