43 research outputs found
Gradient-less Federated Gradient Boosting Trees with Learnable Learning Rates
The privacy-sensitive nature of decentralized datasets and the robustness of
eXtreme Gradient Boosting (XGBoost) on tabular data raise the needs to train
XGBoost in the context of federated learning (FL). Existing works on federated
XGBoost in the horizontal setting rely on the sharing of gradients, which
induce per-node level communication frequency and serious privacy concerns. To
alleviate these problems, we develop an innovative framework for horizontal
federated XGBoost which does not depend on the sharing of gradients and
simultaneously boosts privacy and communication efficiency by making the
learning rates of the aggregated tree ensembles learnable. We conduct extensive
evaluations on various classification and regression datasets, showing our
approach achieves performance comparable to the state-of-the-art method and
effectively improves communication efficiency by lowering both communication
rounds and communication overhead by factors ranging from 25x to 700x.Comment: Accepted at the 3rd ACM Workshop on Machine Learning and Systems
(EuroMLSys), May 8th 2023, Rome, Ital
Machine Learning for Microcontroller-Class Hardware -- A Review
The advancements in machine learning opened a new opportunity to bring
intelligence to the low-end Internet-of-Things nodes such as microcontrollers.
Conventional machine learning deployment has high memory and compute footprint
hindering their direct deployment on ultra resource-constrained
microcontrollers. This paper highlights the unique requirements of enabling
onboard machine learning for microcontroller class devices. Researchers use a
specialized model development workflow for resource-limited applications to
ensure the compute and latency budget is within the device limits while still
maintaining the desired performance. We characterize a closed-loop widely
applicable workflow of machine learning model development for microcontroller
class devices and show that several classes of applications adopt a specific
instance of it. We present both qualitative and numerical insights into
different stages of model development by showcasing several use cases. Finally,
we identify the open research challenges and unsolved questions demanding
careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa
Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives
Consumer's privacy is a main concern in Smart Grids (SGs) due to the
sensitivity of energy data, particularly when used to train machine learning
models for different services. These data-driven models often require huge
amounts of data to achieve acceptable performance leading in most cases to
risks of privacy leakage. By pushing the training to the edge, Federated
Learning (FL) offers a good compromise between privacy preservation and the
predictive performance of these models. The current paper presents an overview
of FL applications in SGs while discussing their advantages and drawbacks,
mainly in load forecasting, electric vehicles, fault diagnoses, load
disaggregation and renewable energies. In addition, an analysis of main design
trends and possible taxonomies is provided considering data partitioning, the
communication topology, and security mechanisms. Towards the end, an overview
of main challenges facing this technology and potential future directions is
presented
Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks
The tremendous achievements of Artificial Intelligence (AI) in computer
vision, natural language processing, games and robotics, has extended the reach
of the AI hype to other fields: in telecommunication networks, the long term
vision is to let AI fully manage, and autonomously drive, all aspects of
network operation. In this industry vision paper, we discuss challenges and
opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To
understand how AI can be successfully landed in current and future networks, we
start by outlining challenges that are specific to the networking domain,
putting them in perspective with advances that AI has achieved in other fields.
We then present a system view, clarifying how AI can be fitted in the network
architecture. We finally discuss current achievements as well as future
promises of AI in networks, mentioning a roadmap to avoid bumps in the road
that leads to true large-scale deployment of AI technologies in networks