Post-Doctoral Research Visit F/M Cooperative Inference Strategies

San Francisco, CA, United States

Post-Doctoral Research Visit F/M Cooperative Inference Strategies Level of qualifications required : PhD or equivalent

Fonction : Post-Doctoral Research Visit

About the research centre or Inria department The Inria centre at Université Côte d'Azur includes 37 research teams and 8 support services. The centre's staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The teams are mainly located on the university campuses of Sophia Antipolis and Nice as well as Montpellier, in close collaboration with research and higher education laboratories and establishments (Université Côte d'Azur, CNRS, INRAE, INSERM ...), but also with the regiona economic players.

With a presence in the fields of computational neuroscience and biology, data science and modeling, software engineering and certification, as well as collaborative robotics, the Inria Centre at Université Côte d'Azur is a major player in terms of scientific excellence through its results and collaborations at both European and international levels.

Context This PostDos is funded by the c hallenge Inria-Nokia Bell Labs: LearnNet (Learning Networks)

Researchers involved

Assignment Introduction

An increasing number of applications rely on complex inference tasks based on machine learning(ML). Currently, two options exist to run such tasks: either served directly by the end device (e.g.,smartphones, IoT equipment, smart vehicles) or offloaded to a remote cloud. Both options may beunsatisfactory for many applications: local models may have inadequate accuracy, while the cloudmay fail to meet delay constraints. In [SSCN+24], researchers from the Inria NEO and Nokia AIRLteams presented the novel idea of inference delivery networks (IDNs), networks of computing nodesthat coordinate to satisfy ML inference requests achieving the best trade-off between latency andaccuracy. IDNs bridge the dichotomy between device and cloud execution by integrating inference delivery at the various tiers of the infrastructure continuum (access, edge, regional data center, cloud).Nodes with heterogeneous capabilities can store a set of monolithic machine-learning models withdifferent computational/memory requirements and different accuracy and inference requests that canbe forwarded to other nodes if the local answer is not considered accurate enough.

Research goal

Given an AI model’s placement in an IDN, we will study inference delivery strategies to be implementedat each node in this task. For example, a simple inference delivery strategy is to provide the inferencefrom the local AI model if this seems to be accurate enough or to forward the input to a more accuratemodel at a different node if the inference quality improvement (e.g., in terms of accuracy) compensatesfor the additional delay or resource consumption. Besides this serve-locally-or-forward policy, we willinvestigate more complex inference delivery strategies, which may allow inferences from models atdifferent clients to be combined. To this purpose, we will rely on ensemble learning approaches [MS22]like bagging [Bre96] or boosting [Sch99], adapting them to IDN distinct characteristics. For example,in an IDN, models may or may not be trained jointly, may be trained on different datasets, and havedifferent architectures, ruling out some ensemble learning techniques. Moreover, queries to remotemodels incur a cost, which leads to prefer ensemble learning techniques that do not require jointevaluation of all available models.

In an IDN, models could be jointly trained on local datasets using federated learning algorithms[KMA+21]. We will study how the selected inference delivery strategy may require changes to such algorithms to consider the statistical heterogeneity induced by the delivery strategy itself. For example,

nodes with more sophisticated models will receive inference requests for difficult samples from nodeswith simpler and less accurate models, leading to a change in the data distribution seen at inferencewith respect to that of the local dataset. Some preliminary results about the training for early-exit

networks in this context are in [KSR+24].

1

References

[Bre96] Leo Breiman. Bagging predictors. Machine Learning, 24(2):123–140, August 1996.

[KMA+21] Peter Kairouz et al, Advances andOpen Problems in Federated Learning. Foundations and Trends in Machine Learning,14(1–2):1–210, 2021.

[KSR+24] Caelin Kaplan, Tareq Si Salem, Angelo Rodio, Chuan Xu, and Giovanni Neglia. Federatedlearning for cooperative inference systems: The case of early exit networks, 2024.

[MS22] Ibomoiye Domor Mienye and Yanxia Sun. A Survey of Ensemble Learning: Concepts,Algorithms, Applications, and Prospects. IEEE Access, 10:99129–99149, 2022.

[Sch99] Robert E. Schapire. A brief introduction to boosting. In Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2, IJCAI’99, pages 1401–1406,San Francisco, CA, USA, July 1999. Morgan Kaufmann Publishers Inc.

[SSCN+24]T. Si Salem, G. Castellano, G. Neglia, F. Pianese and A. Araldo, "Toward Inference Delivery Networks: Distributing Machine Learning With Optimality Guarantees," in IEEE/ACM Transactions on Networking, vol. 32, no. 1, pp. 859-873, Feb. 2024

Main activities Research.

If the selected candidate is interested, he/she may be involved in students' supervision (master and PhD level) and teaching activities.

Skills Candidates must hold a Ph.D. in Applied Mathematics, Computer Science or a closely related discipline. Candidates must also show evidence of research productivity (e.g. papers, patents, presentations, etc.) at the highest level.

We prefer candidates who have strong mathematical background (on optimization, statistical learning or privacy) and in general are keen on using mathematics to model real problems and get insights. The candidate should also be knowledgeable on machine learning and have good programming skills. Previous experiences with PyTorch or TensorFlow is a plus.

Benefits package Partial reimbursement of public transport costs

Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)

Possibility of teleworking (after 6 months of employment) and flexible organization of working hours

Professional equipment available (videoconferencing, loan of computer equipment, etc.)

Social, cultural and sports events and activities

Theme/Domain : Optimization, machine learning and statistical methods

System & Networks(BAP E)

Warning : you must enter your e-mail address in order to save your application to Inria. Applications must be submitted online on the Inria website. Processing of applications sent from other channels is not guaranteed.

Instruction to apply Defence Security :

This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.

Recruitment Policy :

As part of its diversity policy, all Inria positions are accessible to people with disabilities.

Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.

#J-18808-Ljbffr