The Rashomon effect describes the observation that in machine learning (ML) multiple models often achieve similar predictive performance while explaining the underlying relationships in different ways. This observation holds even for intrinsically interpretable models, such as Generalized Additive Models (GAMs), which offer users valuable insights into the model's behavior. Given the existence of multiple GAM configurations with similar predictive performance, a natural question is whether we can personalize these configurations based on users' needs for interpretability. In our study, we developed an approach to personalize models based on contextual bandits. In an online experiment with 108 users in a personalized treatment and a non-personalized control group, we found that personalization led to individualized rather than one-size-fits-all configurations. Despite these individual adjustments, the interpretability remained high across both groups, with users reporting a strong understanding of the models. Our research offers initial insights into the potential for personalizing interpretable ML.

Federated Learning (FL) is an area of research that develops methods to allow multiple parties to collaboratively train machine learning models without exchanging data. First introduced in 2016 by McMahan et al. to allow a large number of edge devices to collaboratively train language models [1], FL has been successfully applied to several domains where for regulatory or privacy reasons models cannot be trained on centralized pooled data. Most FL approaches result in a single collaboratively trained global model that is used by every client for inference. Personalized Federated Learning (PFL) recognizes that in some non-IID contexts performance improvements are possible if each client somehow adapts or personalizes the global model to its data. Approaches range from clients fine-tuning the global model on private data to client clustering, and others discussed in Section 2. In this paper, we build on prior work [2] and explore a simple personalization approach that avoids training a global model which is then personalized. Instead, clients employ privacy-preserving techniques [3] to train a model on their data and make it public to the federation.

Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model due to better generalizability. To address this challenge, this work proposes Flow, a fine-grained stateless personalized FL approach. Flow creates dynamic personalized models by learning a routing mechanism that determines whether an input instance prefers the local parameters or its global counterpart. Thus, Flow introduces per-instance routing in addition to leveraging per-client personalization to improve accuracies at each client. Further, Flow is stateless which makes it unnecessary for a client to retain its personalized state across FL rounds. This makes Flow practical for large-scale FL settings and friendly to newly joined clients. Evaluations on Stackoverflow, Reddit, and EMNIST datasets demonstrate the superiority in prediction accuracy of Flow over state-of-the-art non-personalized and only per-client personalized approaches to FL.

Federated learning (FL) is a distributed machine learning approach involving multiple clients collaboratively training a shared model. Such a system has the advantage of more training data from multiple clients, but data can be non-identically and independently distributed (non-i.i.d.). Privacy and integrity preserving features such as differential privacy (DP) and robust aggregation (RA) are commonly used in FL. In this work, we show that on common deep learning tasks, the performance of FL models differs amongst clients and situations, and FL models can sometimes perform worse than local models due to non-i.i.d. data. Secondly, we show that incorporating DP and RA degrades performance further. Then, we conduct an ablation study on the performance impact of different combinations of common personalization approaches for FL, such as finetuning, mixture-of-experts ensemble, multi-task learning, and knowledge distillation. It is observed that certain combinations of personalization approaches are more impactful in certain scenarios while others always improve performance, and combination approaches are better than individual ones. Most clients obtained better performance with combined personalized FL and recover from performance degradation caused by non-i.i.d. data, DP, and RA.

What comes to your mind when you think of AI-driven personalization in retail and commerce? Do salespeople recognize you the minute you walk into the store and give recommendations based on your purchase history? Perhaps the dressing rooms are equipped with smart displays which show you complementary products to the outfits you've already chosen to try on? Maybe a conversational bot alerts you even before you decided to go shopping that your favorite brand of wine is in stock again and available on aisle 12? We might achieve these futuristic-sounding goals in 5 years or so, but you also need to understand what's actually possible today with AI-personalization of your customer experience. What are the challenges that retail stores and e-commerce companies need to overcome to truly personalize their customers' experience?

Then, what they need is a systematic, comprehensive and scalable hyper-personalization approach. In this not-to-be missed webinar, Anil Kaul, Co-Founder & CEO and Rajat Narang, Associate Director at Absolutdata, will present a contrarian view on how AI impacts personalized marketing, they will also set the foundation for moving to an Artificial Intelligence based hyper-personalized marketing approach, including a real world example of a hospitality leader that saw a 12% incremental revenue through hyper personalization.