Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models

Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in terms of fairness and computational efficiency due to the rising heterogeneity of edges, and thus usually results in sub-optimal performance in recent state-of-the-art (SOTA) solutions. In this paper, we propose a Customized Federated Learning (CFL) system to eliminate FL heterogeneity from multiple dimensions. Specifically, CFL tailors personalized models from the specially designed global model for each client jointly guided by an online trained model-search helper and a novel aggregation algorithm. Extensive experiments demonstrate that CFL has full-stack advantages for both FL training and edge reasoning and significantly improves the SOTA performance w.r.t. model accuracy (up to 7.2% in the non-heterogeneous environment and up to 21.8% in the heterogeneous environment), efficiency, and FL fairness.Comment: 8 pages, 7 figure

Scheduling for Weighted Flow and Completion Times in Reconfigurable Networks

New optical technologies offer the ability to reconfigure network topologies dynamically, rather than setting them once and for all. This is true in both optical wide area networks (optical WANs) and in datacenters, despite the many differences between these two settings. Because of these new technologies, there has been a surge of both practical and theoretical research on algorithms to take advantage of them. In particular, Jia et al. [INFOCOM '17] designed online scheduling algorithms for dynamically reconfigurable topologies for both the makespan and sum of completion times objectives. In this paper, we work in the same setting but study an objective that is more meaningful in an online setting: the sum of flow times. The flow time of a job is the total amount of time that it spends in the system, which may be considerably smaller than its completion time if it is released late. We provide competitive algorithms for the online setting with speed augmentation, and also give a lower bound proving that speed augmentation is in fact necessary. As a side effect of our techniques, we also improve and generalize the results of Jia et al. on completion times by giving an $O(1)$-competitive algorithm for arbitrary sizes and release times even when nodes have different degree bounds, and moreover allow for the weighted sum of completion times (or flow times).Comment: 10 pages. Appears in INFOCOM 202

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie

Falkor: Federated Learning Secure Aggregation Powered by AES-CTR GPU Implementation

We propose a novel protocol, Falkor, for secure aggregation for Federated Learning in the multi-server scenario based on masking of local models via a stream cipher based on AES in counter mode and accelerated by GPUs running on the aggregating servers. The protocol is resilient to client dropout and has reduced clients/servers communication cost by a factor equal to the number of aggregating servers (compared to the naïve baseline method). It scales simultaneously in the two major complexity aspects: 1) large number of clients; 2) highly complex machine learning models such as CNNs, RNNs, Transformers, etc. The AES-CTR-based masking function in our aggregation protocol is built on the concept of counter-based cryptographically-secure pseudorandom number generators (csPRNGs) as described in [SMDS\u2711] and subsequently used by Facebook for their torchcsprng csPRNG. We improve upon torchcsprng by careful use of shared memory on the GPU device, a recent idea of Cihangir Tezcan [Tezcan\u2721] and obtain 100x speedup in the masking function compared to a single CPU core. In addition, we prove the semantic security of the AES-CTR-based masking function. Finally, we demonstrate scalability of our protocol in two real-world Federated Learning scenarios: 1) efficient training of large logistic regression models with 50 features and 50M data points distributed across 1000 clients that can dropout and securely aggregated via three servers (running secure multi-party computation (SMPC)); 2) training a recurrent neural network (RNN) model for sentiment analysis of Twitter feeds coming from a large number of Twitter users (more than 250,000 users). In case 1), our secure aggregation algorithm runs in less than a minute compared to a pure MPC computation (on 3 parties) that takes 27 hours and uses 400GB RAM machines as well as 1 gigabit-per-second network. In case 2), the total training is around $10$ minutes using our GPU powered secure aggregation versus 10 hours using a single CPU core

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort

Achieving Causal Fairness in Recommendation

Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed systems utilize user-item interaction data to train models and then generate new data by online recommendation. This feedback loop in recommendation often results in various biases in observational data. The goal of this dissertation is to address challenging issues in achieving causal fairness in recommender systems: achieving user-side fairness and counterfactual fairness in bandit-based recommendation, mitigating confounding and sample selection bias simultaneously in recommendation and robustly improving bandit learning process with biased offline data. In this dissertation, we developed the following algorithms and frameworks for research problems related to causal fairness in recommendation. • We developed a contextual bandit algorithm to achieve group level user-side fairness and two UCB-based causal bandit algorithms to achieve counterfactual individual fairness for personalized recommendation; • We derived sufficient and necessary graphical conditions for identifying and estimating three causal quantities under the presence of confounding and sample selection biases and proposed a framework for leveraging the causal bound derived from the confounded and selection biased offline data to robustly improve online bandit learning process; • We developed a framework for discrimination analysis with the benefit of multiple causes of the outcome variable to deal with hidden confounding; • We proposed a new causal-based fairness notion and developed algorithms for determining whether an individual or a group of individuals is discriminated in terms of equality of effort