Despite its pilot progress, personalized federated learning with differential privacy still faces numerous challenges.

Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those common models to the specifics of each client's local data distribution. However, different FL problems may require different personalization strategies, and it may not even be possible to define an effective one-size-fits-all personalization strategy for all clients: Depending on how similar each client's optimal predictor is to that of the global model, different personalization strategies may be preferred. In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics. By learning these meta-nets through FL, we allow the whole FL network to collaborate in learning a customized personalization strategy for each client. Empirical results show that this framework improves on a range of standard hand-crafted personalization baselines in both label and feature shift situations.

In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics.

We conducted three 90 - minute workshops at Talenthub Op Zuid, each with a different group of participants (total N=24, MAge =17.6, SD=1.2, see S upplement for additional details). In the first workshop, participants reviewed the prior 13 personas from Stage 1 and critiqued them for gaps in relevance. The scoping personas generated from survey and forum data gave youth stakeholders a concrete starting point for consulting as experts by experience in initial co - design activities. They challenged the realism of the scoping personas . Using fill - in - the - blank templates to guide but not restrict their persona creation (created by a youth member of the research team with design training, see Supplement), youth added contextual details to the project personas, such as daily routines, stressors, and digital habits, and brainstormed plausible backstories involving bullying, school difficulties, or parental conflict. The second workshop engaged a new participant group who expanded on previous outputs and addressed additional questions on living environment and emotional support needs, as this was suggested as relevant by youth from the prior workshop . Participants revised or created new personas b ased on their own or peers' experiences. In t he third workshop, a new group of participants again reviewed prior co - creation and outputs and further refined the personas .

In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics.

Online intelligent education platforms have generated a vast amount of distributed student learning data. This influx of data presents opportunities for cognitive diagnosis (CD) to assess students' mastery of knowledge concepts while also raising significant data privacy and security challenges. To cope with this issue, federated learning (FL) becomes a promising solution by jointly training models across multiple local clients without sharing their original data. However, the data quality problem, caused by the ability differences and educational context differences between different groups/schools of students, further poses a challenge to the fairness of models. To address this challenge, this paper proposes a fairness-aware federated cognitive diagnosis framework (FedCD) to jointly train CD models built upon a novel parameter decoupling-based personalization strategy, preserving privacy of data and achieving precise and fair diagnosis of students on each client. As an FL paradigm, FedCD trains a local CD model for the students in each client based on its local student learning data, and each client uploads its partial model parameters to the central server for parameter aggregation according to the devised innovative personalization strategy. The main idea of this strategy is to decouple model parameters into two parts: the first is used as locally personalized parameters, containing diagnostic function-related model parameters, to diagnose each client's students fairly; the second is the globally shared parameters across clients and the server, containing exercise embedding parameters, which are updated via fairness-aware aggregation, to alleviate inter-school unfairness. Experiments on three real-world datasets demonstrate the effectiveness of the proposed FedCD framework and the personalization strategy compared to five FL approaches under three CD models.

Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those common models to the specifics of each client's local data distribution. However, different FL problems may require different personalization strategies, and it may not even be possible to define an effective one-size-fits-all personalization strategy for all clients: Depending on how similar each client's optimal predictor is to that of the global model, different personalization strategies may be preferred. In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics. By learning these meta-nets through FL, we allow the whole FL network to collaborate in learning a customized personalization strategy for each client. Empirical results show that this framework improves on a range of standard hand-crafted personalization baselines in both label and feature shift situations.

Joel is Head of Sales & Marketing at Vestorly, an AI-driven content curation engine. He writes about leveraging content effectively. Content marketing has long been a passive form of marketing. Email personalization, on the other hand, is an active approach. Email marketing reflects a staple in our personal and professional lives.

Personalization has become integral to the customer journey and is now a key driver of brand loyalty across all channels. Consumers are much more likely to buy from brands – both in-store and online – when offers are personalized. And it's not just your brand communications that need to be more relevant: consumers are also interested in purchasing more personalized products and services, and are willing to wait longer to get them. You know more about your customers than ever before. But isn't one of your biggest challenges how to make sense of all that customer data so your marketing messages can be more targeted and relevant?