The fields of market mechanisms, blockchain protocols, federated learning, and machine learning are witnessing significant advancements towards designing more efficient and fair systems. A common theme among these areas is the incorporation of game theoretical models and differential privacy techniques to analyze and optimize the behavior of participants. Notably, researchers are exploring new approaches to resource distribution, transaction fee mechanisms, and posted pricing in large markets. The use of buying rights, extensive-form game models, and posted price mechanisms are being investigated to improve fairness, efficiency, and scalability. For instance, a repeated hybrid market with buying rights has been proposed, which reduces frustration by at least half compared to the free market. In the realm of federated learning, novel aggregation strategies and frameworks are being developed to enhance the resilience and privacy guarantees of federated learning systems. Harm-centered frameworks are being explored to link fairness definitions to concrete risks and stakeholder vulnerabilities. Moreover, measuring participant contributions in decentralized federated learning is crucial for incentivizing clients and ensuring transparency. Machine learning is also moving towards greater emphasis on privacy and security, with a focus on differential privacy. New techniques and algorithms are being developed to ensure that machine learning models can be trained and deployed while protecting sensitive data. Differentially private algorithms for tasks such as bandits, federated learning, and optimization problems are being designed to provide strong privacy guarantees while maintaining model performance. Furthermore, federated learning is becoming more efficient and privacy-preserving, with adaptive methods and multimodal federated learning gaining attention. Cost-aware and serverless workflows are being explored to optimize resource utilization and reduce expenses in FL environments. Overall, these advancements are paving the way for more efficient, fair, and private systems in various fields.