Albertsons

Federated learning (FL) enables collaborative model training across distributed data sources while preserving privacy, but deploying and operating FL systems at scale remains highly complex. Most existing frameworks emphasize algorithms rather than the practical challenges of provisioning cloud resources, configuring communication layers, and managing heterogeneous client environments, resulting in configuration drift, operational inefficiencies, and limited reproducibility. This paper presents a GitOps driven control plane that expresses the entire FL topology including servers, aggregators, clients, networking, and storage as declarative infrastructure. The system automates multi cloud provisioning, maintains continuous configuration consistency, and applies training aware self healing to remediate failures without manual intervention. Evaluation across multiple cloud environments demonstrates significant gains in provisioning speed, operational reliability, and reproducible deployment. The proposed approach offers a unified and automated foundation for managing large scale FL infrastructures and supports more dependable operation of distributed learning systems.

Multi-cluster cloud platforms introduce complex system-level resource management challenges in which actions taken within one cluster can propagate demand shifts, contention, and failure impacts across others. While cluster-local autoscaling and reactive controllers are effective for localized elasticity, they lack the global context required for coordinated capacity shaping, workload placement, and fairness across clusters operating under heterogeneous policies, network conditions, and regional constraints. This paper formulates multi-cluster resource optimization as a constrained, system-level control problem and presents a stability-aware federated coordination framework. The framework constructs a global system view from cross-cluster telemetry, computes admissible allocation and placement actions under policy, quota, and cost constraints, and regulates actuation using bounded step sizes and damping to ensure predictable convergence and avoid oscillatory behavior. A Kubernetes-based prototype deployed across geographically distributed clusters demonstrates reduced cross-cluster load skew, improved fairness, and faster system-level stabilization compared to cluster-isolated control, while maintaining bounded coordination overhead. These results underscore the necessity of stability-aware system-level coordination for reliable and efficient operation of large-scale multi-cluster cloud environments.

Multi-agent systems powered by large language models are increasingly used to automate complex workflows involving data processing, infrastructure operations, and service orchestration. However, agent-tool interoperability in such systems is often implemented through informal schemas and prompt-embedded conventions, leading to fragile integrations, limited observability, and high recovery costs under interface evolution. This paper presents a contract-driven approach to operationalizing multi-agent interoperability using the Model Context Protocol (MCP). We propose an MCP-mediated architecture that formalizes agent-tool interactions through explicit capability descriptors, schema validation, and semantic version negotiation, while providing structured telemetry and failure isolation. The design decouples agent reasoning from tool integration, enabling independent evolution of agents and tools without breaking compatibility. We evaluate a prototype implementation using representative multi-agent automation workflows across multiple datasets with controlled schema drift. Experimental results demonstrate a $\mathbf{7. 4 \%}$ improvement in integration success rate, a 60.6% reduction in mean time to recovery, and a 4.4 times increase in schema mismatch detection precision, with a P95 mediation latency overhead of 18.4 ms. These results show that contract-driven MCP mediation provides a practical and scalable foundation for building reliable, evolvable, and observable multi-agent systems in production environments.

Balancing data privacy with model performance remains a central challenge in deploying machine learning systems on sensitive datasets. This paper presents an experimental analysis of three widely used syntactic data anonymization techniques k -anonymity, l -diversity, and t -closeness and evaluates their impact on both privacy protection and downstream machine learning utility. Using the Adult Census and German Credit benchmark datasets, we systematically measure privacy outcomes through re-identification risk and attribute disclosure risk, and utility outcomes through classification accuracy, AUC, and information loss across multiple learning models. The results demonstrate that $\mathbf{t}$-closeness provides the strongest privacy guarantees, achieving up to 94.2% reduction in re-identification risk, but incurs the highest degradation in model performance. K -anonymity preserves the greatest utility while exhibiting significant vulnerability to attribute disclosure under skewed sensitive attribute distributions. L-diversity consistently offers a balanced tradeoff, maintaining moderate privacy protection with acceptable utility loss. The study further analyzes the influence of dataset characteristics, model choice, and computational overhead on anonymization effectiveness. Based on empirical findings, we derive practical guidelines to support informed selection of anonymization techniques according to application sensitivity and performance requirements, contributing actionable insights for privacy-aware machine learning practice