Federated Learning (FL) exhibits susceptible to model poisoning attacks, which compromise the availability of the collaboratively trained model by introducing detrimental local updates during the training process. The predominant line of defense against such attacks has been to impose stringent restrictions on clients' model updates. However, this strategy raises new vulnerabilities where the global model can be infiltrated by meticulously crafted malicious perturbations. This vulnerability arises due to the model's inherent sensitivity to perturbations, making it exposed and fragile. In response, this work investigates a novel defensive paradigm centered on model stability-specifically, a model's resilience against perturbations within its parameter space. As a solution, we introduce a new method named Model Stability Defense for Federated Learning (MSDFL), designed to fortify the defense of FL systems against model poisoning attacks. MSDFL utilizes a minmax optimization framework, which is fundamentally linked to empirical risk for exploring the effects of model perturbations. The core aim of our approach is to minimize the norm of the model-output Jacobian matrix without compromising predictive performance, thereby establishing defense through enhanced model stability. Moreover, we propose a refined version of MSDFL, named Holistic Model Stability Defense for Federated Learning (HMSDFL), which considers model stability across all output dimensions of the logits to effectively eradicate the disparity in model convergence speed induced by MSDFL. Extensive experimental results fully demonstrate the fidelity, robustness, compatibility, and self-protection of our methods.

It is well-known that different applications exhibit varying amounts of ILP. Execution of these applications on the same fixed-width VLIW processor will result (1) in wasted energy due to underutilized resources if the issue-width of the processor is larger than the inherent ILP; or alternatively, (2) in lower performance if the issue-width is smaller than the inherent ILP. Moreover, even within a single application distinct phases can be observed with varying ILP and therefore changing resource requirements.With this in mind, we designed the ρ-VEX processor, which is a VLIW processor that can change its issuewidth
at run-time. In this paper, we propose a novel scheme to dynamically (i.e., at run-time) optimize the resource utilization by predicting and matching the number of active data-paths for each application phase. The purpose is to achieve low energy consumption for applications with low ILP, and high performance for applications with high ILP, on a single VLIW processor design. We prototyped the ρ-VEX processor on an FPGA and obtained the dynamic traces of applications running on top of a Linux port. Our results show that it is possible in some cases to achieve the performance of an 8-issue core with 10% lower energy consumption, while in others we achieve the energy consumption
of a 2-issue core with close to 20% lower execution time.