Communication Efficient Fair and Robust Federated Learning

Yaodong Yu, Sai Praneeth Karimireddy, Yi Ma, and Michael I. Jordan, Scaff-PD: Communication Efficient Fair and Robust Federated Learning, arXiv:2307.13381.

We present Scaff-PD, a fast and communication-efficient algorithm for distributionally robust federated learning. Our approach improves fairness by optimizing a family of distributionally robust objectives tailored to heterogeneous clients. We leverage the special structure of these objectives, and design an accelerated primal dual (APD) algorithm which uses bias corrected local steps (as in Scaffold) to achieve significant gains in communication efficiency and convergence speed. We evaluate Scaff-PD on several benchmark datasets and demonstrate its effectiveness in improving fairness and robustness while maintaining competitive accuracy. Our results suggest that Scaff-PD is a promising approach for federated learning in resource-constrained and heterogeneous settings.

Optimization of carbon emission reduction paths in the low-carbon power dispatching process

J. Jin, Q. Wen, S. Cheng, Y. Qiu, X. Zhang, and X. Guo, Optimization of carbon emission reduction paths in the low-carbon power dispatching process, Renewable Energy, Volume 188, April 2022, Pages 425-436.

With the development of low-carbon electricity, the scale of wind power is expanding continuously and carbon trading for thermal power is popularized gradually. In this context, the optimal combination of thermal power and wind power needs to be further promoted to build up the synergy of carbon reduction. To solve such low-carbon power dispatching problem in the wind power integrated system imported with carbon trading, this paper firstly presents a distributed robust optimization model. Next, the scenario-based characterization of wind power and allocation methods of initial carbon emission rights are discussed for model solution. Finally, empirical analysis shows that: (1) the proposed model proves to be rational and feasible, which can accomplish a good compromise between economy, environment and robustness of power system, (2) wind power integration dose help carbon reduction ratio to achieve up to 50% with lower operating costs and carbon emissions, while carbon trading is really an effective approach for tapping greater carbon reduction potential of thermal power, and (3) more reasonable proportions of wind power in coping with its inherent uncertainties, and more appropriate cooperation modes of thermal power for dealing with carbon trading unpredictability are determined under the different requirement of carbon reduction.

National energy system optimization modelling for decarbonization pathways analysis

F.A. Plazas-Niño, N.R. Ortiz-Pimiento, E.G. Montes-Páez, National energy system optimization modelling for decarbonization pathways analysis: A systematic literature review, Renewable and Sustainable Energy Reviews, Volume 162, July 2022, 112406.

Energy planning is fundamental to ensure a sustainable, affordable, and reliable energy mix for the future. Energy system optimization models (ESOMs) are the accurate tools to guide decision-making in national energy planning. This article presents a systematic literature review covering the main ESOMs, the input and output data involved, the trends in scenario analysis for decarbonization pathways in national economies, and the challenges associated with energy system optimization modelling. The first part introduces the characterization of ESOMs, showing a trend in modelling focused on long-term, multisector, multiperiod, bottom-up, linear programming, and perfect foresight. Secondly, the analysis shows the intensive data requirements, including future demand profiles, fuel price projections, energy potentials, and techno-economic characteristics of technologies. This review also reveals that decarbonization pathways are the principal objective in energy system optimization modelling, including key drivers such as high-share renewable energy integration, energy efficiency increase, sector coupling, and sustainable transport. The last section presents ten challenges and their corresponding opportunities in research, highlighting the improvement of spatiotemporal resolution, transparency, the inclusion of social aspects, the representation of developing country features, and quality and availability data.

Deep Learning Interviews

Shlomo Kashani and Amir Ivry,  Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI, arXiv:2201.00650

use ChatGPT to learn ***

Jessica Shieh, Best practices for prompt engineering with OpenAI API: How to give clear and effective instructions to GPT-3 and Codex, OpenAI. (API)

Esor Huang，ChatGPT提示語進階說明書：通用三層結構＋九個技巧，輕鬆讓AI完成複雜的任務，關鍵評論網，2023/04/28

janus，30年資深美國律師用ChatGPT當他的助手寫訴狀，結果法官、對方律師、甚至連他自己都傻了，techbang，2023年6月10日 (產生不存在的案例)

• 屬於非線性規劃的章節，不知道同學怎麼問的，ChatGPT 給的答案是用決策樹求解？

Carbon dioxide capture, transport and storage supply chains

Viola Becattini, Paolo Gabrielli, Cristina Antonini, Jordi Campos, Alberto Acquilino, Giovanni Sansavini, Marco Mazzotti, Carbon dioxide capture, transport and storage supply chains: Optimal economic and environmental performance of infrastructure rollout, International Journal of Greenhouse Gas Control, Volume 117, June 2022, 103635.

This work presents a novel optimization framework for the optimal design of carbon capture, transport, and storage supply chains in terms of installation, sizing and operation of carbon dioxide (CO2) capture and transport technologies. The optimal design problem is formulated as a mixed-integer linear program that minimizes the total costs of the supply chains while complying with different emissions reduction pathways over a deployment time horizon of 25 years. All design decisions are time-dependent and are taken with a yearly resolution. Whereas the model is general, here its features are illustrated by designing optimal supply chains to decarbonize the Swiss waste-to-energy sector, for various emission reduction pathways, when up to two storage sites are considered, namely one in the North Sea assumed to be already available and a hypothetical one in Switzerland assumed to be possibly available in the future. Findings show that, unless a domestic storage site becomes available soon, the transport cost is the greatest contribution to the overall costs, followed by the capture cost, while the storage cost plays only a minor role. Pipelines are the most cost-effective mode of transport for large volumes of transported CO2, especially when considering multi-year time horizons for the planning of the supply chains. Ship and barge connections are competitive with pipeline connections, whereas rail and truck connections are cost-optimal only when considering shortsighted time horizons or small volumes of CO2 transported.

Queueing Theory: Classical and Modern Methods

Dimitris Bertsimas and David Gamarnik, Queueing Theory: Classical and Modern Methods, ‎Dynamic Ideas, 2022.

STRUCTURE OF THE BOOK:

• Part I describes single and multi-server queues.
• Part II treats single and multiclass queueing networks (MQNETs).
• Part III introduces asymptotic methods, including queueing networks in heavy traffic, large deviations, call centers, queues in space, and the supermarket model.
• Part IV outlines the use of optimization in queueing networks.
• Part V presents Markov chains and processes, Brownian motion, and weak convergence in the Appendix.