Zahir Alsulaimawi

Zahir Alsulaimawi

Federated learning protocols require repeated synchronization between clients and a central server, with convergence rates depending on learning rates, data heterogeneity, and client sampling. This paper asks whether iterative communication is necessary for distributed linear regression. We show it is not. We formulate federated ridge regression as a distributed equilibrium problem where each client computes local sufficient statistics -- the Gram matrix and moment vector -- and transmits them once. The server reconstructs the global solution through a single matrix inversion. We prove exact recovery: under a coverage condition on client feature matrices, one-shot aggregation yields the centralized ridge solution, not an approximation. For heterogeneous distributions violating coverage, we derive non-asymptotic error bounds depending on spectral properties of the aggregated Gram matrix. Communication reduces from $\mathcal{O}(Rd)$ in iterative methods to $\mathcal{O}(d^2)$ total; for high-dimensional settings, we propose and experimentally validate random projection techniques reducing this to $\mathcal{O}(m^2)$ where $m \ll d$. We establish differential privacy guarantees where noise is injected once per client, eliminating the composition penalty that degrades privacy in multi-round protocols. We further address practical considerations including client dropout robustness, federated cross-validation for hyperparameter selection, and comparison with gradient-based alternatives. Comprehensive experiments on synthetic heterogeneous regression demonstrate that one-shot fusion matches FedAvg accuracy while requiring up to $38\times$ less communication. The framework applies to kernel methods and random feature models but not to general nonlinear architectures.

Zahir Alsulaimawi, Huaping Liu

Federated learning (FL) lets distributed nodes train a shared model without exchanging their raw data, but in privacy-sensitive deployments medical sensors, IoT devices, wearables the protection offered by keeping data local is incomplete: gradients, model updates, and the released representations themselves can leak sensitive attributes. We propose the \emph{Gaussian Privacy Protector} (GPP), a data-release framework for continuous, high-dimensional inputs that learns a stochastic encoder mapping raw data to a low-dimensional sanitized representation. The encoder is trained against a variational lower bound on the mutual information between the released representation and a designated sensitive attribute, while a separate cross-entropy term preserves a designated utility attribute, with a Lagrange multiplier $β$ controlling the trade-off. We then extend GPP to the federated setting, in which each client trains a local encoder, sensitive labels never leave the client, and the aggregator receives only sanitized representations giving instance-level privacy protection in addition to the standard ``raw data stays local'' guarantee of FL. We evaluate GPP on MNIST (digit-sum utility, parity sensitive), CelebA (smiling vs.\ gender), and HAPT-Recognition (activity vs.\ subject identity). Across all three benchmarks, GPP attains utility within roughly one percentage point of an unconstrained autoencoder baseline while reducing the adversary's AUC to near random guessing.

Zahir Alsulaimawi

This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks. We propose a simplified consensus-based verification process integrated with an adaptive thresholding mechanism. This dynamic thresholding is designed to adjust based on the evolving landscape of model updates, offering a refined layer of anomaly detection that aligns with the real-time needs of distributed learning environments. Our method necessitates a majority consensus among participating clients to validate updates, ensuring that only vetted and consensual modifications are applied to the global model. The efficacy of our approach is validated through experiments on two benchmark datasets in deep learning, CIFAR-10 and MNIST. Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience. This method transcends conventional techniques that depend on anomaly detection or statistical validation by incorporating a verification layer reminiscent of blockchain's participatory validation without the associated cryptographic overhead. The innovation of our approach rests in striking an optimal balance between heightened security measures and the inherent limitations of FL systems, such as computational efficiency and data privacy. Implementing a consensus mechanism specifically tailored for FL environments paves the way for more secure, robust, and trustworthy distributed machine learning applications, where safeguarding data integrity and model robustness is critical.

Zahir Alsulaimawi

Real-time scene comprehension is a key advance in artificial intelligence, enhancing robotics, surveillance, and assistive tools. However, hallucination remains a challenge. AI systems often misinterpret visual inputs, detecting nonexistent objects or describing events that never happened. These errors, far from minor, threaten reliability in critical areas like security and autonomous navigation where accuracy is essential. Our approach tackles this by embedding self-awareness into the AI. Instead of trusting initial outputs, our framework continuously assesses them in real time, adjusting confidence thresholds dynamically. When certainty falls below a solid benchmark, it suppresses unreliable claims. Combining YOLOv5's object detection strength with VILA1.5-3B's controlled language generation, we tie descriptions to confirmed visual data. Strengths include dynamic threshold tuning for better accuracy, evidence-based text to reduce hallucination, and real-time performance at 18 frames per second. This feedback-driven design cuts hallucination by 37 percent over traditional methods. Fast, flexible, and reliable, it excels in applications from robotic navigation to security monitoring, aligning AI perception with reality.

Zahir Alsulaimawi

Federated Learning (FL) enables collaborative model training across diverse entities while safeguarding data privacy. However, FL faces challenges such as data heterogeneity and model diversity. The Meta-Federated Learning (Meta-FL) framework has been introduced to tackle these challenges. Meta-FL employs an optimization-based Meta-Aggregator to navigate the complexities of heterogeneous model updates. The Meta-Aggregator enhances the global model's performance by leveraging meta-features, ensuring a tailored aggregation that accounts for each local model's accuracy. Empirical evaluation across four healthcare-related datasets demonstrates the Meta-FL framework's adaptability, efficiency, scalability, and robustness, outperforming conventional FL approaches. Furthermore, Meta-FL's remarkable efficiency and scalability are evident in its achievement of superior accuracy with fewer communication rounds and its capacity to manage expanding federated networks without compromising performance.