Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.Comment: Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-08

POTENTIAL OF MULTI-TEMPORAL UAV-BORNE LIDAR IN ASSESSING EFFECTIVENESS OF SILVICULTURAL TREATMENTS

Silvicultural treatments are practiced to control resource competition and direct forest stand development to meet management objectives. Effective tracking of thinning and partial cutting treatments help in timely mitigation and ensuring future stand productivity. Based on a study conducted in autumn 2015, our findings in a white pine dominant forest stand in Petawawa (Ontario, Canada) showed that almost all individual trees were detectable, structure of individual trees and undergrowth was well pronounced and underlying terrain below dense undisturbed canopy was well captured with UAS based Riegl Vux-1 lidar even at a range of 150 m. Thereafter, the site was re-scanned the following summer with the same system. Besides understanding the difference in distribution patterns due to foliage conditions, co-registering the two datasets, in the current study, we tested the potential of quantifying effectiveness of a partial cutting silvicultural system especially in terms of filling of 3D spaces through vertical or lateral growth and mortality in a very short period of time

POTENTIAL OF UAV BASED CONVERGENT PHOTOGRAMMETRY IN MONITORING REGENERATION STANDARDS

Several thousand hectares of forest blocks are regenerating after harvest in Canada. Monitoring their performance over different stages of growth is critical in ensuring future productivity and ecological balance. Tools for rapid evaluation can support timely and reliable planning of interventions. Conventional ground surveys or visual image assessments are either time intensive or inaccurate, while alternate operational remote sensing tools are unavailable. In this study, we test the feasibility and strength of UAV-based photogrammetry with an EO camera on a UAV platform in assessing regeneration performance. Specifically we evaluated stocking, spatial density and height distribution of naturally growing (irregularly spaced stems) or planted (regularly spaced stems) conifer regeneration in different phases of growth. Standard photogrammetric workflow was applied on the 785 acquired images for 3D reconstruction of the study sites. The required parameters were derived based on automated single stem detection algorithm developed in-house. Comparing with field survey data, preliminary results hold promise. Future studies are planned to expand the scope to larger areas and different stand conditions