99 research outputs found
Enabling Social Applications via Decentralized Social Data Management
An unprecedented information wealth produced by online social networks,
further augmented by location/collocation data, is currently fragmented across
different proprietary services. Combined, it can accurately represent the
social world and enable novel socially-aware applications. We present
Prometheus, a socially-aware peer-to-peer service that collects social
information from multiple sources into a multigraph managed in a decentralized
fashion on user-contributed nodes, and exposes it through an interface
implementing non-trivial social inferences while complying with user-defined
access policies. Simulations and experiments on PlanetLab with emulated
application workloads show the system exhibits good end-to-end response time,
low communication overhead and resilience to malicious attacks.Comment: 27 pages, single ACM column, 9 figures, accepted in Special Issue of
Foundations of Social Computing, ACM Transactions on Internet Technolog
Complement Sparsification: Low-Overhead Model Pruning for Federated Learning
Federated Learning (FL) is a privacy-preserving distributed deep learning
paradigm that involves substantial communication and computation effort, which
is a problem for resource-constrained mobile and IoT devices. Model
pruning/sparsification develops sparse models that could solve this problem,
but existing sparsification solutions cannot satisfy at the same time the
requirements for low bidirectional communication overhead between the server
and the clients, low computation overhead at the clients, and good model
accuracy, under the FL assumption that the server does not have access to raw
data to fine-tune the pruned models. We propose Complement Sparsification (CS),
a pruning mechanism that satisfies all these requirements through a
complementary and collaborative pruning done at the server and the clients. At
each round, CS creates a global sparse model that contains the weights that
capture the general data distribution of all clients, while the clients create
local sparse models with the weights pruned from the global model to capture
the local trends. For improved model performance, these two types of
complementary sparse models are aggregated into a dense model in each round,
which is subsequently pruned in an iterative process. CS requires little
computation overhead on the top of vanilla FL for both the server and the
clients. We demonstrate that CS is an approximation of vanilla FL and, thus,
its models perform well. We evaluate CS experimentally with two popular FL
benchmark datasets. CS achieves substantial reduction in bidirectional
communication, while achieving performance comparable with vanilla FL. In
addition, CS outperforms baseline pruning mechanisms for FL
Concept Matching: Clustering-based Federated Continual Learning
Federated Continual Learning (FCL) has emerged as a promising paradigm that
combines Federated Learning (FL) and Continual Learning (CL). To achieve good
model accuracy, FCL needs to tackle catastrophic forgetting due to concept
drift over time in CL, and to overcome the potential interference among clients
in FL. We propose Concept Matching (CM), a clustering-based framework for FCL
to address these challenges. The CM framework groups the client models into
concept model clusters, and then builds different global models to capture
different concepts in FL over time. In each round, the server sends the global
concept models to the clients. To avoid catastrophic forgetting, each client
selects the concept model best-matching the concept of the current data for
further fine-tuning. To avoid interference among client models with different
concepts, the server clusters the models representing the same concept,
aggregates the model weights in each cluster, and updates the global concept
model with the cluster model of the same concept. Since the server does not
know the concepts captured by the aggregated cluster models, we propose a novel
server concept matching algorithm that effectively updates a global concept
model with a matching cluster model. The CM framework provides flexibility to
use different clustering, aggregation, and concept matching algorithms. The
evaluation demonstrates that CM outperforms state-of-the-art systems and scales
well with the number of clients and the model size
To be Tough or Soft: Measuring the Impact of Counter-Ad-blocking Strategies on User Engagement
The fast growing ad-blocker usage results in large revenue decrease for
ad-supported online websites. Facing this problem, many online publishers
choose either to cooperate with ad-blocker software companies to show
acceptable ads or to build a wall that requires users to whitelist the site for
content access. However, there is lack of studies on the impact of these two
counter-ad-blocking strategies on user behaviors. To address this issue, we
conduct a randomized field experiment on the website of Forbes Media, a major
US media publisher. The ad-blocker users are divided into a treatment group,
which receives the wall strategy, and a control group, which receives the
acceptable ads strategy. We utilize the difference-in-differences method to
estimate the causal effects. Our study shows that the wall strategy has an
overall negative impact on user engagements. However, it has no statistically
significant effect on high-engaged users as they would view the pages no matter
what strategy is used. It has a big impact on low-engaged users, who have no
loyalty to the site. Our study also shows that revisiting behavior decreases
over time, but the ratio of session whitelisting increases over time as the
remaining users have relatively high loyalty and high engagement. The paper
concludes with discussions of managerial insights for publishers when
determining counter-ad-blocking strategies.Comment: In Proceedings of The Web Conference 2020 (WWW 20
- …