  Locality-sensitive hashing (LSH) based frameworks have been used efficiently
to select weight vectors in a dense hidden layer with high cosine similarity to
an input, enabling dynamic pruning. While this type of scheme has been shown to
improve computational training efficiency, existing algorithms require repeated
randomized projection of the full layer weight, which is impractical for
computational- and memory-constrained devices. In a distributed setting,
deferring LSH analysis to a centralized host is (i) slow if the device cluster
is large and (ii) requires access to input data which is forbidden in a
federated context. Using a new family of hash functions, we develop one of the
first private, personalized, and memory-efficient on-device LSH frameworks. Our
framework enables privacy and personalization by allowing each device to
generate hash tables, without the help of a central host, using device-specific
hashing hyper-parameters (e.g. number of hash tables or hash length). Hash
tables are generated with a compressed set of the full weights, and can be
serially generated and discarded if the process is memory-intensive. This
allows devices to avoid maintaining (i) the fully-sized model and (ii) large
amounts of hash tables in local memory for LSH analysis. We prove several
statistical and sensitivity properties of our hash functions, and
experimentally demonstrate that our framework is competitive in training
large-scale recommender networks compared to other LSH frameworks which assume
unrestricted on-device capacity.
