| 1. |
- Fallahi, Sara, 1985-, et al.
(författare)
-
Financing solutions for circular business models : Exploring the role of business ecosystems and artificial intelligence
- 2023
-
Ingår i: Business Strategy and the Environment. - : John Wiley and Sons Ltd. - 0964-4733 .- 1099-0836. ; 32:6
-
Tidskriftsartikel (refereegranskat)abstract
- The circular economy promotes a transition away from linear modes of production and consumption to systems with circular material flows that can significantly improve resource productivity. However, transforming linear business models to circular business models posits a number of financial consequences for product companies as they need to secure more capital in a stock of products that will be rented out over time and therefore will encounter a slower, more volatile cash flow in the short term compared to linear direct sales of products. This paper discusses the role of financial actors in circular business ecosystems and alternative financing solutions when moving from product-dominant business models to Product-as-a-Service (PaaS) or function-based business models. Furthermore, the paper demonstrates a solution where state-of-the-art artificial intelligence (AI) modeling can be incorporated for financial risk assessment. We provide an open implementation and a thorough empirical evaluation of an AI-model, which learns to predict residual value of stocks of used items. Furthermore, the paper highlights solutions, managerial implications, and potentials for financing circular business models, argues the importance of different forms of data in future business ecosystems, and offers recommendations for how AI can help mitigate some of the challenges businesses face as they transition to circular business models. © 2022 The Authors.
|
|
| 2. |
- Isaksson, Martin, et al.
(författare)
-
Adaptive Expert Models for Federated Learning
- 2023
-
Ingår i: <em>Lecture Notes in Computer Science </em>Volume 13448 Pages 1 - 16 2023. - Cham : Springer Science and Business Media Deutschland GmbH. - 9783031289958 ; 13448 LNAI, s. 1-16
-
Konferensbidrag (refereegranskat)abstract
- Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive. However, the state-of-the-art solutions in this framework are not optimal when data is heterogeneous and non-IID. We propose a practical and robust approach to personalization in FL that adjusts to heterogeneous and non-IID data by balancing exploration and exploitation of several global models. To achieve our aim of personalization, we use a Mixture of Experts (MoE) that learns to group clients that are similar to each other, while using the global models more efficiently. We show that our approach achieves an accuracy up to 29.78% better than the state-of-the-art and up to 4.38% better compared to a local model in a pathological non-IID setting, even though we tune our approach in the IID setting. © 2023, The Author(s)
|
|
| 3. |
- Listo Zec, Edvin, et al.
(författare)
-
Decentralized Adaptive Clustering of Deep Nets is Beneficial for Client Collaboration
- 2023
-
Ingår i: FL 2022. - Cham : Springer Nature. ; , s. 59-71
-
Konferensbidrag (refereegranskat)abstract
- We study the problem of training personalized deep learning models in a decentralized peer-to-peer setting, focusing on the setting where data distributions differ between the clients and where different clients have different local learning tasks. We study both covariate and label shift, and our contribution is an algorithm which for each client finds beneficial collaborations based on a similarity estimate for the local task. Our method does not rely on hyperparameters which are hard to estimate, such as the number of client clusters, but rather continuously adapts to the network topology using soft cluster assignment based on a novel adaptive gossip algorithm. We test the proposed method in various settings where data is not independent and identically distributed among the clients. The experimental evaluation shows that the proposed method performs better than previous state-of-the-art algorithms for this problem setting, and handles situations well where previous methods fail.
|
|
