  As an indispensable personalized service in Location-based Social Networks
(LBSNs), the next Point-of-Interest (POI) recommendation aims to help people
discover attractive and interesting places. Currently, most POI recommenders
are based on the conventional centralized paradigm that heavily relies on the
cloud to train the recommendation models with large volumes of collected users'
sensitive check-in data. Although a few recent works have explored on-device
frameworks for resilient and privacy-preserving POI recommendations, they
invariably hold the assumption of model homogeneity for parameters/gradients
aggregation and collaboration. However, users' mobile devices in the real world
have various hardware configurations (e.g., compute resources), leading to
heterogeneous on-device models with different architectures and sizes. In light
of this, We propose a novel on-device POI recommendation framework, namely
Model-Agnostic Collaborative learning for on-device POI recommendation (MAC),
allowing users to customize their own model structures (e.g., dimension \&
number of hidden layers). To counteract the sparsity of on-device user data, we
propose to pre-select neighbors for collaboration based on physical distances,
category-level preferences, and social networks. To assimilate knowledge from
the above-selected neighbors in an efficient and secure way, we adopt the
knowledge distillation framework with mutual information maximization. Instead
of sharing sensitive models/gradients, clients in MAC only share their soft
decisions on a preloaded reference dataset. To filter out low-quality
neighbors, we propose two sampling strategies, performance-triggered sampling
and similarity-based sampling, to speed up the training process and obtain
optimal recommenders. In addition, we design two novel approaches to generate
more effective reference datasets while protecting users' privacy.
