The field of differential privacy and decentralized learning is rapidly evolving, with a focus on developing innovative methods to protect data privacy while maintaining model accuracy. Recent research has centered around improving the efficiency and effectiveness of differentially private PAC learners, with notable advancements in reducing sample complexity. Additionally, decentralized learning methods are being designed to dynamically adjust gradient clipping bounds and noise levels, leading to enhanced model accuracy while preserving the total privacy budget. The integration of public-key cryptography and anonymization techniques is also being explored to ensure source anonymity in random walk-based decentralized learning. Furthermore, post-quantum secure decentralized random number generation protocols are being developed to provide publicly verifiable random outputs. Noteworthy papers include: An nearly optimal differentially private PAC learner for concept classes with VC dimension 1, which achieves a sample complexity of O(log* d). Dyn-D^2P, a dynamic differentially private decentralized learning approach that leverages the Gaussian DP framework for privacy accounting, enabling the enhancement of model accuracy while preserving the total privacy budget. Source Anonymity for Private Random Walk Decentralized Learning, which proposes a privacy-preserving algorithm based on public-key cryptography and anonymization to hide the source's identity. Post-Quantum Secure Decentralized Random Number Generation Protocol, which designs a DRNG based on lattice-based publicly verifiable secret sharing that is post-quantum secure and proven secure in the standard model.